mohammadali sharifi

A significant portion of daily urban intra-city trips is aimed at accessing services, amenities, and goods that are not readily available in a specific area. Therefore, analyzing the frequently used trajectory and identifying the reasons for high traffic volumes on these trajectories can lead to a more accurate distribution of facilities, services, and proper land use allocation with the goal of reducing the number, distance, and time of intra-city trips. With the advent of Global Navigation Satellite Systems (GNSS) positioning sensors on smartphones, the real-time collection of individuals' positions, speed, acceleration, and more has become possible. Consequently, this research has sought to examine the possibility of using GNSS data recorded by smartphones to identify the transportation mode used by the user through four supervised machine learning models named Random Forest (RF), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Model (LightGM). For this purpose, two datasets, Microsoft Geolife and MTL 2017, which possess the necessary features for this goal, have been used as the input data. After extracting the features of each trajectory from these two datasets, with the aim of improving the models' performance and reducing processing time, among the available features, the most important ones have been identified, and classification has been applied based on them. Among the models used, the LightGM and XGB models achieved the best performance for the first and second datasets with respective F1-Scores of 92.57% and 92.67% for test data. Out of a total of 1349 trips, this algorithm accurately estimated 1250 trips, contributing to sustainable urban transportation.

Tabriz city, like most big cities in the world, is facing complex problems in terms of population density, air and noise pollution, providing transportation services, social, economic housing and other issues. Therefore, the formation of a smart city based on smart criteria in the city of Tabriz based on big data can be a great help for government and city managers to determine the strategy of a smart city. The reason for the lack of success in the implementation of the smart city is the lack of awareness of the smart city and its management and the effective factors in its formation. Therefore, it is very necessary and vital to identify the effective factors in the formation of a sustainable smart city of Tabriz at this time. The purpose of this study is to identify the effective factors in the development of a sustainable smart city and its characteristics in the city of Tabriz based on a developed model. Achieving urban sustainability through smart cities is necessary to manage urban problems that threaten human survival. Smart city policies include various social, economic, physical and environmental aspects and provide the possibility of developing practical plans for urban sustainability. The purpose of this research is to measure the factors affecting the non-development of a sustainable smart city in Tabriz.

The research method is descriptive-exploratory. It is functional in purpose. The statistical population of the research includes experts and specialists in urban planning and professors. The sample size is 46 people. To analyze the data using the structural equation method, the final part describes the confirmatory factor analysis using the structural equation model (SEM). SPSS software was used for data analysis. SEM modeling was done using AMOS software. The confirmatory factor analysis (CFA) model was conducted using various factors of sustainable smart city development such as traffic problems, parking problems, stormwater problem, waste management, road safety problems, pollution problems, drinking water problems and crime-related problems. AMOS software was used to perform structural equation modeling (SEM).

The results showed that the traffic problem with a factor load of 0.958 has the greatest impact and the flood problem with a factor load of 0.451 has the least impact among the factors affecting the development of the sustainable smart city of Tabriz. Also, the goodness-of-fit index (GFI) was 0.946, which indicated the acceptability of this amount for the optimal fit of the model. The value of the root mean square error of estimation (RMSEA) was 0.057, which is acceptable considering that it is smaller than 0.08, and it indicates the confirmation of the research model. Therefore, it can be concluded that the CFA model is valid, and the selected parameters can contribute to the development of a sustainable smart city in Tabriz.

The biggest challenge in planning and constructing the smart city of Tabriz is identifying the real needs of the people, raising public awareness, especially among urban management, and explaining the mechanisms and benefits of smart initiatives. This includes understanding the people's share of these benefits and promoting public participation in areas such as traffic management, urban waste management, air pollution, and crime-related issues. Neglecting these aspects has negatively impacted the quality of life and general well-being of society, causing disruptions in service provision. This study offers in-depth knowledge on implementing various aspects of the smart city plan and suggests solutions for further developing the factors that contribute to the sustainability of the smart city of Tabriz.

Deformation monitoring is a crucial engineering task for public safety. Any incorrect estimation of displacement rates can cause economical and deadly effects on engineering structures. Three methods have already been developed for structure deformation monitoring in the geodetic community: the single point method, and the robust and combinatorial estimation methods. In this article, the methods were implemented on a simulated dataset of the Global Navigation Satellite System. As a result, the Simultaneous Adjustment of Two Epochs and Multiple Sub Sample using distance differences methods were defined as the most optimal methods to find the stable and unstable points in the simulated network. To show the performance of the methods on a real dataset, the optimal methods were employed on the real GNSS observations collected of a pedestrian steel bridge in Tehran. The GNSS receiver type is LEICA GRX1200+GNSS. Moreover, two scenarios are investigated: all epochs have the same global coordinate systems (Scenario A), and analysis of the earth’s surface movements with the local coordinate system in the first epoch of observations and the global WGS84 coordinate system for the others (Scenario B). The 3-D Helmert transformation was also used to transfer the global coordinate systems to the local one. Scenario B showed better results with a smaller RMS error with an amount of 7e-5.

In this research, three-dimensional and four-dimensional tomography is used to demonstrate the distribution of wet refractivity index of the troposphere. In this model, spherical cap harmonics are used for the horizontal distribution of the wet refractivity index, and empirical orthogonal functions are used for the vertical distribution of the index. The region of study is in the west California State, and the wet refractivity index is retrieved from the wet tropospheric delay measurements. to validate the results, radiosonde profiles were compared to the tomographically retrieved profiles. The result shows that wet refractivity indices can be retrieved using functional models with RMSE about 2.4 ppm till 3.9 in four-dimension method. The comparisons show that the four-dimensional retrieved profiles shows improvement up to 34 and 42 percentage in mid-day tomography epochs compare to three-dimensional tomography results. Also it can be seen that in mid-night epochs three-dimensional tomography has higher accuracy compare to four-dimension method because of low variation of wet refractivity indices

The rotation of the solid Earth with respect to inertial space is not constant due to the changes of external gravitational forces and internal dynamics. Earth orientation parameters (EOP), including, the Earth’s polar motion (PM), Anomalies in the Earth’s angular velocity and celestial pole offsets (CPO), describe these irregularities in the Earth’s rotation. Anomalies in the axis defined by the celestial intermediate pole (CIP) with respect to the Z axis of the terrestrial reference system are named as PM. The CPO are expressed as the deviations, dX and dY, between the observed CIP and the conventional CIP position. The difference between the smoothed principal form of universal time UT1 and the coordinated universal time UTC denotes the Earth’s rotation angle, which together with the xp, yp terrestrial pole coordinates, forms a set of Earth orientation parameters (EOP). In addition to the other EOP, the length of day (LOD) is used to model the Anomalies in the Earth’s rotation rate. LOD is the difference between the duration of the day measured by space geodesy and nominal day of 86,400 s duration.Generally, EOP are the parameters that provide the rotation of the International Terrestrial Reference System (ITRS) to the International Celestial Reference System (ICRS) as a function of time. However, the EOP are computed using the modern space geodetic techniques such as Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI) and the Global Navigation Satellite System (GNSS), they are unavailable to the real-time applications due to the data processing complexities. Accurate and rapid EOP predictions are required for different fields like precise orbit determinations of artificial Earth satellites, positional astronomy, space navigation and geophysical phenomena.There are many different methods for analysis and prediction of EOP time series including deep learning methods, least square (LS) with autoregressive (AR) and also Singular Spectrum Analysis as a non-parametric method.In this research Least Square Harmonic Estimation analysis is used to investigate the frequencies of EOP. First, the solid and ocean tide terms are modeled based on IERS technical notes. These effects are removed from LOD time series. The remained time series are named as LODR time series. The univariate time series analysis is then applied to the LODR time series and multivariate analysis is used for detecting the PM periodic patterns. Applying these methods to the 40 years of observations of EOP (since 1 January 1980 to 31 December 2020) revealed the Chandler, annual, semi Chandler, semi-annual and annual signals as the main periodic signals in the EOP time series. The functional model is then formed using all detected signals in order to model the deterministic variations of EOP time series.In order to model the remained non-deterministic variations, an ARMA (Autoregressive Moving Average) model is fitted to the least square residuals. The Akaike's Information Criterion (AIC) is used to investigate the optimized order of ARMA model.The EOP is then predicted for the first 20 days of 2021, using the pre-identified functional model for the deterministic part and the ARMA model for the non-deterministic part of the time series variations. For the prediction of LOD time series, after creating the functional model of LODR time series, the solid and ocean tide terms are added to the functional model of LODR.Finally, in order to validate the accuracy of the proposed method, a comparison is made with an EOP prediction study that used the ANN (Artificial Neural Network) and ANFIS (Adaptive Network Based Fuzzy Inference System) methods for short term prediction of EOP.The result shows that the accuracy of the proposed method is better than the previous study and the method can be used for accurate prediction of EOP time series.

IntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. Many parameters have an impact on the precision and accuracy of their information. Atmospheric friction is one of the most principal forces on satellites, which may cause deviation and falling of satellite on a short period. From the beginning of aerospace missions, many efforts have been done to determine atmospheric friction by geodesists, e.g., empirical models of atmosphere neutral density. Because of the complex nature of atmosphere behavior and also data limitations, these models may have low accuracy. So, there is a need for methods to improve the accuracy of empirical models by means of combining observations of atmospheric density to predict its future state. Materials & MethodsAlong with the extension of computer science, new reliable algorithms have been introduced which are able to predict a time series; Artificial Intelligent (AI) and Neural Networks (NN) are the best of these methods. These simple algorithms are inspirations of the human brain and its ability to learn and have been used in many different scientific fields. In these techniques without any requirement for constructing complex modeling, the relation between input and output will be provided only using weight and bias vectors during the training procedure. Simple Neural Networks are memoryless meaning that the value of time-series in previous can’t be used for predicting the future value of time series and therefore some important dependency of signal values with time will be lost. A Recurrent Neural Network (RNN) has been implemented to overcome this issue. RNN’s can store some important information of the values of the time series in the previous steps in a chain-like structure and using this information for predicting the next value of time series that will improve the accuracy of prediction. In this study, the Long Short-Term Memory (LSTM) Neural Network which is a kind of Recurrent Neural Network’s has been implemented to predict the scale for correcting atmospheric density of numerical models. The data of Grace Accelerometer observation in the 6 first month of the year 2014 have been used for training the LSTM for univariate training. Also, the LSTM has been trained in multi-variants mode once with using the coefficient of atmospheric correction expansion up to degree 2 and once with using sun geomagnetic information along with information of k_p index. Results & DiscussionAfter training the LSTM network, by using the estimated parameters of the model, the zero degrees coefficient of harmonic expansion for a scale factor of correcting atmospheric density has been predicted in periods of 7, 14, 30, 60, and 90 days. The results of the univariate model show that the lower RMSE (Root Mean Square Error) is obtained about 0.054 in the period of prediction of about 14 days. Also, the results show that the multi-variants model with input data of sun geomagnetic information and k_p index has lower RMSE values in considered prediction periods compared to the other modes and the lowest RMSE is about 0.03 and belongs to the prediction of about 7 days. For evaluation of LSTM parameters in the obtained results, the predictions have been implemented with various Window sizes. The results show that by increasing windows size, the RMSE of the prediction will be reduced and the lowest RMSE was for prediction of 7 days with a window size of about 90 days. For the purpose of more evaluation, with the predicted atmospheric densities correction coefficient, the orbit of GRACE satellites has been propagated and the calculated position and velocity of satellites have been compared with the real orbit data. The results show that the lower RMSE will be provided with the prediction of 7 days with an RMSE for position and velocity of about 50 meters and 0.15 m/s respectively. ConclusionIn this study, due to the complex nature of the atmosphere, the LSTM Neural Network has been used for modeling and predict the zero-order scale for correcting atmospheric densities harmonic expansion. For training the network, the data of Grace Satellites Accelerometer in the 180 days of the year 2014 have been used. The LSTM has been in univariate and multi-variant models. In the multi-variants model, once with using the coefficient of atmospheric correction expansion up to degree two and once with using sun geomagnetic information along with information of k_p index the network have been trained. The period of prediction was considered of about 7, 14, 30, 60, and 90 days.The results show that the LSTM is capable to predict the correction coefficient in considered periods with a mean RMSE of about 0.05 for zero-order degree. Also, the results show that the lowest RMSE was for the 7 and 14 days of prediction and by increasing the window size of LSTM the RMSE will be decreased. The results of calculating the position of GRACE satellites position and velocity using predicted correction coefficients with real data show that the lowest RMSE was for prediction of 7 days for implemented method.

The coastline is considered as the boundary between water and land. coasts are one of the most important environmental effects that directly affect human life. Rising sea levels due to global warming have made coastal cities among the areas under threat. Therefore, management and planning to prevent coastal erosion is one of the important issues that should be considered. In this research, we have studied the changes of shoreline using sentinel-1 satellite images in mazandaran province, the coast between the cities of chalous and tonekabon. For this purpose, two images on 15/01/2019 and 01/09/2021 have been used and the desired images have been taken from the google earth engine system. In the pre-processing step of the images, we corrected the height error of the study area and adjusted the speckle noise. The edge of the shore was discovered with the otsu threshold in the images. Finally, Digital Shoreline analysis system (DSAS) was used to calculate the amount of sedimentation and erosion of the coast by EPR technique. In 87% of the coast of this transects, we have witnessed coastal erosion. The average amount of erosion is about 7 meters per year.

Atmospheric wet refractivity indices, which are dependent on the water vapor, are one of the most important parameters for analyzing climate change in an area. Wet refractivity indices can be estimated from Radiosonde stations measurement or calculated from numerical meteorological models. But due to low temporal and spatial resolution of radiosonde stations and severe variations of water vapor in the lower levels of Atmosphere, today’s numerical meteorological models provide low accuracy for atmospheric parameters. But nowadays, by growing number of stations that can use global positioning satellite measurements, atmospheric parameter can be estimated via remote sensing measurements in wide temporal and spatial resolutions. Wet refractivity indices cause delay in GPS measurement signals thus this delay have information about distribution of wet refractivity indices in atmosphere. By the use of global positioning satellites that can estimate atmospheric wet delay and tomography method, wet refractivity indices can be estimated. One of the growing methods for measuring the atmosphere parameters is the radio occultation technique. By increasing the number of low earth orbit satellites that carry GNSS receiver, this technique can provide observation in all of the globe, which its observations are obtained directly from the type of atmosphere parameters. The aim of this study is to use a combination of RO and GPS observation in 3D and 4D atmospheric tomography. But since tomography problem are ill-posed because of the poor distribution of GPS observations in network, a functional model has been implemented to estimate the wet refractivity indices from of the atmospheric tomography problem. By expanding tomography’s unknowns to base functions coefficients, the number of unknowns will be decreased and problem will become well-posed and unknowns can be estimated from inverse problem. In the three-dimensional functional model, combination of spherical cap harmonics and empirical orthogonal functions have been used to solve the inverse problem. Spherical cap harmonics are used to represent the wet refractivity indices in horizontal distribution and empirical orthogonal functions are used for the vertical distribution of the unknown coefficients. Eventually, the B-spline is used to represent the four-dimensional functional model and the dependence of coefficients to the time. After implementing 3D and 4D functional models, the relative weight of RO data with comparison to GPS data has been calculated using variance component method. The US region of California has been selected as the study network due to its high tectonic importance and the large number of GPS stations. The results in two considered tomography epochs have been validated with radiosonde station data in the network and also have been compared with ERA5 reanalysis data. Comparison of the profiles obtained from tomography and the ERA5 data profiles with the radiosonde wet refractivity indices shows that the results obtained from the functional model tomography are better than those of the ERA5 data. The results of the combination illustrate that using RO data in both 3D and 4D models, the RMSE has been decreased and showed improvement of about 7 to 10 percent compared to uncombined tomographic models. Also, it is seen that using RO data in the 4D model has higher accuracy compared to the 3D model due to the use of a time-dependent functional model that increases the functional model's accuracy.

The Gravity Recovery and Climate Experiment (GRACE) mission and its successor, GRACE Follow-On, have been observing the Earth’s static and time-variable gravity field with unprecedented accuracy from 2002, thanks to the precision equipment used, such as very accurate ranging systems, dual-frequency GPS receivers, star cameras and highly sensitive accelerometers. The accelerometers on board of these missions provide high quality measurements of the non-gravitational forces acting on the satellites, provided that they are calibrated. In this paper, a wavelet-based detrending scheme is used to estimate drift and bias of GRACE accelerometer data. This method is applied to a simulated noisy time series and two sets of GRACE accelerometer data (recorded on January 1, 2005 and during March 2015). The results confirm the speed and ease of the proposed method due to the nature of the wavelet-based detrending scheme. The estimated bias and drift parameters have acceptable accuracies because the wavelet-based method does not require any reference value and its results are not affected by uncertainties in gravitational field modeling. Furthermore, some computational problems such as the amplification of noise during the numerical differentiation of satellite positions do not exist. In addition, the accelerometer readouts, provided in the Science Reference Frame (SRF), may be calibrated without applying any coordinate transformation.

In recent years, the development of the country in the space industry and the ability of building, launching and infusion of satellites in the lower orbit has put the limited number of countries with such technology. In order to complete the entire cycle of the space industry, the satellite navigation and control, which has been neglected since the beginning of the movement of space science in the country, has to be considered specially. The attitude determination in one sentence is the application of a variety of techniques for estimating the attitude of spacecrafts. In dynamic astronomy, the attitude determination is the the process of controlling the orientation of an aerospace vehicle with respect to an inertial frame of reference or another entity such as the celestial sphere, certain fields, and nearby objects, etc. A spacecraft attitude determination and control system typically uses a variety of sensors and actuators. Because attitude is described by three or more attitude variables, the di®erence between the desired and measured states is slightly more complicated than for a thermostat, or even for the position of the satellite in space. Furthermore, the mathematical analysis of attitude determination is complicated by the fact that attitude determination is necessarily either underdetermined or overdetermined.

Attitude determination typically requires finding three independent quantities, such as any minimal parameterization of the attitude matrix. The mathematics behind attitude determination can be broadly characterized into approaches that use stochastic analysis and approaches that do not. This paper considers a computationally efficient algorithm to optimally estimate the spacecraft attitude from vector observations taken at a single time, which is known as single-point or single-frame attitude determination method. There have been a number of attitude determination algorithms that compute optimal attitude of a spacecraft from various observation sources (known as the Wahba’s problem), and each of the methods has advantages and limitations in terms of accuracy and computational speed. The most popular are: the very important ˆq-Method, the most popular TRIAD and QUEST, SVD, FOAM, and ESOQ-1, the fastest ESOQ-2, and many others approaches introducing new insights or different characteristics, for instance, the EAA, Euler-2, Euler-ˆq, and OLAE.

Since star detection algorithms can provide more than two stars, the star detector field of view often consists of two or more stars that are passed through the identification algorithms will be detected, those star vectors that have measurement errors can be compensated by using more than two stars. Methods such as the QUEST algorithm usually optimize an error function to the minimum optimal. In fact, the QUEST algorithm estimates the optimum specific eigenvalue and vector for the problem described in the Q_method method without the need for complex numerical calculations. The fact that the QUEST algorithm retains all the computational advantages of a fast definitive algorithm while maintaining the desired result efficiency underscores why it is typically used.

Simulation results showed that the traid and quest algorithms with shuster method attitude determination algorithm can be an efficient alternative over the eight tested algorithm in terms of computational efficiency for singularity-free attitude representation.

The orbit determination in one sentence is the application of a variety of techniques for estimating the orbits of objects such as the moon, planets and spacecraft. In dynamic astronomy, the orbit determination is the process of determining orbital parameters with observations. Considering the visibility of the satellite motion trace and the fundamental need to determine and modify satellites’ orbital parameters as well as identify special satellites, determining the positional parameters of the satellite is also one of the modern and important applications of vision-based astronomical systems. In the modern vision-based astronomical systems, data collection is done using a charge-coupled device (CCD) array. In this paper, a new method is presented for satellite streak detection through an optical imaging system. This automatic and efficient method, which has the ability of real-time data analysis, is based on the sidereal image using CCDs. The images captured by this method have a large amount of information about stars, galaxy, and satellites’ streaks. In this paper, an automatic method is presented for streak detection. The purpose of this research is to find an optimal method for satellite streak detection and different methods in clustering such as k_means, particle swarm optimization (PSO), genetic algorithm (GA), and Gaussian mixture model (GMM). Finally, some assessment criteria were compared and concluded that GA is an optimal algorithm in satellite streak detection.

standard processing schemes of altimetry lead to the solution with relatively lower accuracy in shallow waters and inland water bodies. In order to get more accurate solution one needs to process the data with more specific processing algorithms. In this paper, we intend to review some retracking methods for shallow water level determination. Moreover, we introduce a new method for time series of water level using the well-known outlier detection of Baarda hypothesis testing. The achieved results are then compared with the dataset which is publicly available from Technical University of Denmark (TUD) as the standard data. The results show that the water levels estimated by the Baarda test are consistent with those of the robust mean water level. The accuracy of results obtained by the new method is at the same order of the robust mean water levels. the differences of baarda method with the standard method and tidegauge data in optimistic method are 2.7 and 28 cm.

Modern missions such as CHAMP, GRACE and GOCE which derive the Earth’s static and time-variable gravity field with unprecedented accuracy with monthly or even sub-monthly resolution, are also sensitive to short-term (weekly or shorter) non-tidal mass variations due to mass transports and mass redistribution phenomena in the atmosphere, the oceans and the continental water storage. GRACE derived gravity solutions contain errors mostly due to instrument noise, anisotropic spatial sampling and temporal aliasing caused by incomplete reduction of short-term mass variations in models. Improving the quality of satellite gravimetry observations, in term of using more sensitive sensors and increasing the spatial isotropy, has been discussed in the context of the designed scenarios of GRACE-Follow On (GRACE-FO) mission. Temporal aliasing is still a factor that affects the quality of the gravity field. For GRACE data processing only the short-term variations are of importance, because with the monthly Grace gravity field solutions it is planned to provide data for determination of the seasonal variations. Short-term mass variations cannot be measured adequately by GRACE. Therefore they are removed from measurements beforehand using geophysical models (de-aliasing). This paper specifically focuses on the atmosphere of Earth and its mass variations using the ITG-3D method. In this paper, various type of data such as the atmospheric pressure parameter, the multilevel geopotential, temperature and humidity parameters from European Center for Medium-Range Weather Forecasts (ECMWF) have been used to perform three-dimensional integral solution. ERA-Interim and ERA5 reanalysis are considered as the datasets. In the procedure of calculations, the shape of earth is approximated as an ellipse. As a first step in calculation procedure, it is necessary to remove the effect of long-term variations. In order to eliminate this effect; the mean variations of atmospheric mass over a specific period should be subtracted from the mass variation. Atmospheric de-aliasing products can be illustrated as sets of spherical harmonic coefficients, which are estimated using atmospheric mass variations. Then, the effect of atmospheric mass changes on geoid height and vertical deformation were calculated. In the computation, the ECMWF data on 1 January 2015 at 00:00h were used, while the mean atmospheric mass variations were derived from the means of the years 2015 and 2016. The results of the comparison between two datasets demonstrated that the maximum differences in parameters are located in Asia and Antarctic. The results indicate that the mean of difference between atmospheric mass variations from ERA-Interim and ERA5 is 0.23 kgm-2. The results show that the difference between the coefficients is about one percent of their values. In addition, the geoid height from ERA5 changes on average of -0.16 cm whereas this parameter varies on average -0.17 cm using ERA-Interim data due to atmospheric mass variations. The difference of vertical deformation from two datasets is -0.002 cm on average. The atmospheric mass variations calculated by the two data sets (ERA-Interim and ERA5) is not significantly different. The validation results of the vertical deformation of the two data also show a high correlation with the GPS time series.

One of the parameters that can be used to predict different types' behavior of civil structures is the displacement components, extracted from structures. Bridges and their health monitoring system are important parts of the land transportation system because of their safety and durability. This study aims to evaluate the safety behavior of the Tabiat pavement bridge using a high-rate geodetic monitoring of the Global Positioning System (GPS). Accurate orbital information (SP3) is also used to process the collected observations as well as the Double Difference Bernese software. Firstly, the data are processed kinematically, using the coordinate conversion system, into the local coordinates of the bridge (N, E, and H). At the beginning, the raw data is pre-processed using the Kalman filter to reduce noise. The time series of GPS observations are estimated with 0.0013 m accuracy by applying the Kalman filter with CWPA method. Afterwards, the MA filter, mean time zone and Median filter are used to estimate the semi-static, static and dynamic components of bridge movements, respectively. The results obtained by applying the MA filter indicate that the semi-static component is extracted with 0.005 m accuracy in the North, 0.003 m in the East and 0.01 m in the Height directions. The Median filter also estimates the dynamic component with precision of 0.0007, 0.0006 and 0.001 m in three directions N, E and H, respectively. In this study, considering the sampling rate of observations, the average of 5 minutes of semi-static movement is considered as a static behavior of each point. In order to extract the dominant frequencies of the bridge, the least squares harmonic estimation (LSHE) method is used. Finally the Artificial Neural Network model (ANN) with 5 hidden layers and 5 delays is used to predict bridge deformation based on data mining. The frequencies at points on the bridge are approximately similar, which shows the similarity of the periodogram to the high hardness of the bridge. In addition, the frequencies in both North and East directions, relative to Height direction, indicate the GPS sensitivity to load effects. The prediction model using the artificial neural network is then fitted to dynamic component, with accuracy of approximately 6×10-5m and 4×10-5m to the dynamic and semi-static components of the bridge motion, respectively. Both the least squares harmonic estimation method and the ANN model are considered as suitable techniques for estimating the performance changes of GPS measurements in the frequency and time domains during the monitoring time period. The results indicate that the bridge behavior safety design range the minimum changes using GPS measurements in the time and frequency domains. Besides, the frequency analysis of the bridge movement time series and the neural network model, can be used to detect significant frequency changes and study the bridge performance rigidity, respectively. These results show that high-frequency satellite geodetic data which properly processed can also be useful for measuring the dynamic displacements of tall buildings, cable-stayed bridges, flexible and rigid civil structures. Also they would be the bases of bridge safety monitoring system primary warning.

To extract valid results from time series analysis of tides observations, noise reduction is vital. This study aimed to use a precise statistical model to investigate noise types. Noise component amplitude of the proposed model was studied by Least Square Estimation (LS-VCE) through different statistical models: (1) white noise and auto-regressive noise, (2) white noise and Flicker noise, (3) white noise and random walk noise, (4) white noise and Flicker noise and random walk, and (5) auto-regressive noise and Flicker noise. Based on the values obtained for the Likelihood Function, it was concluded that the noise model that can be considered for observations of the Buoy time series includes two white and Flicker noises. In addition, tide forecasting for all stations was done by extracting important frequency calculated in two cases: (1) the first case in which matrix of observation weight matrix was considered as the unit matrix or the noise model was just a white noise (2) the case in which matrix of observation weight matrix was considered as a combination of white and Flicker noises. The results show that use of precise observation weight matrix resulted in 11 millimeter difference compared to the case in which observation with unit weight matrix was used.

Nowadays, engineering structures face many threats. Natural and human activities can result in deformation and displacement of dams, bridges, and towers. As a result, any crack in the body of these structures is important and may have dangerous consequences. To prevent catastrophes, the behavior of these structures should be monitored permanently during the construction phase and after opening.Nowadays,thebehavior of engineering structures such as dams, power plants, and towers is considered to be especially important. Three different methods are usually used to measure such behavior: classical, satellite and precise instruments.

Modern equipment is considered to be a crucial factor in controllingpossible changes and preventing human errors. Therefore, different sensors are installed in the structure to measure tensile and shear flexibility during the construction phase. Moreover, data received from these sensors is analyzed permanently during the service life to ensure sustainability of the structure. These tools make internal analysis of these structures possible. Analyzing the behavior of engineering structures is considered to be one of the most important tasks in the field of geodesy. Inaccurate analysis of displacements can have deadly effects. Various methods are used to measure such displacements, which are divided into two categories: robust and non-robust methods based upon the results of the epoch adjustment. To find deformations, a geodetic network should be defined in the first step. If two epochs are not measured in the same datum, the results will not be reliable. Displacement can be measured in two ways: Absolute and Relative. In the absolute method, some points are considered to be stable, while in the relative network, all points are considered to be unstable, and the problem is solved based upon this hypothesis. The method of relative network is used in the present study. Regarding network geometry, displacement analysis is performed using two

single and combinatorial. Moreover, displacement analysis is divided into two categories of robust and non-robust methods. Iterative Weighted Similarity Transformation (IWST)and Minimum L1 norm are among robust methods which calculate the matrix of displacement by minimizing the first and second norm. Global Congruency Test (GCT) is a non-robust statistical method used to determine unstable points in geodetic networks. Robust and GCT are among classical methods used to discover unstable points in geodetic networks, while Simultaneous Adjustment of Two Epoch (SATE(is a new method used to achieve this purpose. Combinatorial methods are also considered to be a suitable alternative method used for detecting unstable points in a geodetic network. In our previous study, “evaluation of single-point methods used fordetecting displacement in classical geodetic networks”, single-point methods of detecting unstable points were investigated and the SATE method was selected as the optimal method. Unlike single-point methods, these methods examine all points of the geodetic network simultaneously to discover unstable points.

The strong dependence of these methods on the network geometry makes discovery of all unstable points impossible. Combinatorial methods are considered to be a suitable alternative method used to detect all unstable points in the geodetic network. These methods does not have a strong dependence on scale and the network geometry. Multiple Sub Sample and M-split methods are classified in this category. These methods can detect unstable points efficiently. The present study takes advantage of simulated datato evaluate combinatorial methods such as Multiple Sub Sample (MSS) Angles, MSS-distance difference, and M-split and compare them with the SATE method with the aim of choosing the optimal method. Then, unstable points in the real network of Jamishan dam in Kermanshah Province will be discovered using the identified optimal method.

The present study identifies the best method between single and combinatorial methods. The best method can detect most unstable points and has the lowest dependence on geometry, scale and other factors influencing the results.According to the results, Multiple Sub Sample with distance difference is selected as the best method.

The problem of datum transformation; determination of parameters for transferring curvilinear coordinate from one ellipsoid to another, is one of the main problems in geometrical geodesy. The problem draws the attentions of many researchers due to its role in the integration of all types of data in the geospatial database framework. Although the problem is one of the oldest geometrical problems by its nature, it is still challenging because of the newly introduced Earth gravitational models and precise global coordinate measurements using the global positioning systems. Different methods have been introduced by many famous geodesists like Molodensky (1962), Vanicek (1986) and others.In this paper, we developed a full mathematical model for determination of datum transformation parameters based on ellipsoidal approximation. It is theoretically and numerically compared with the previously developed model with spherical approximation. For small area, both models lead to the same accuracy while we expect to achieve higher with the ellipsoidal approximation in wider area.     Moreover, lack of ellipsoidal height in the old data sets is one of the main obstacles for the implementation of the classical transformation schemes. Herein, we introduced two methods for solving this problem. The Earth Gravitational Models (EGMs) which were wieldy available in the new century, thanks to the Earth gravity field’s dedicated missions, were employed to get an estimate of the geoidal heights of the data point with enough accuracy. Alternatively, the idea of the widely used polynomial approximating correcting surface was considered to model the geoid height at the area of computation. The numerical results showed that the second alternative was most helpful. Higher accuracy and better fitness in terms of statistical goodness of fit criteria were the outcomes of the implementation of the polynomial approximating correcting surface.     In order to show the performance of the ellipsoidal approximation as well as the idea of polynomial correcting surface, 150 points were selected in the Nigeria. The curvilinear coordinates of the data points were given both in the CLARCK-1880 (local old coordinates) and the World Geodetic System 1984 (WGS84) as the global new coordinates. The old coordinates of the data points were geodetic latitudes, geodetic longitudes and orthometric heights where the new coordinate set is fully geodetic components. A quadratic polynomial mathematical model was employed to approximate the geoid surface in the country. The achieved results showed its reasonable accuracy.

Groundwater aquifers are one of the most significant freshwater resources in the world. Hence, the monitoring of these resources is particularly important for available water resources planning. Piezometric wells have traditionally been used to monitor groundwater. This approach is costly and pointwise, which is not feasible for places with steep topography and mountainous areas. Nowadays, remote sensing techniques are widely used in various fields of engineering as appropriate alternatives to traditional methods. In water resources management, the Gravity Recovery and Climate Experiment (GRACE) satellites can monitor groundwater changes with acceptable accuracies. This paper applied the GRACE satellite data for a 40-month period to assess the variation of the groundwater level in Khuzestan province. The Global Land Data Assimilation System (GLDAS) model was used to counteract the soil moisture effect in final results. The observed data from piezometric wells were pre-processed to detect outliers using the Mahalanobis algorithm in Khuzestan province. At last, the outputs of GRACE were compared with these processed observed data. Despite the relatively small size of the area in question, the results indicated the efficiency of GRACE data (RMSE = 0.8, NRMSE = 0.2) for monitoring the groundwater level changes.

A new approach with the ability to use the multiple observations based on the least square approach has been proposed for initial orbit determination. This approach considers the Earth’s Oblateness by using the developed Lagrange coefficients. The efficiency of the proposed method has been tested in two scenarios. The first scenario is to use the simulated and the second one is to utilize the real angle-only observations for the GRACE-like and GPS-like satellites. Under the first scenario, the ground-based observations are produced using the reduced-dynamic orbit generated by GFZ. Then, various error levels were added to the produced azimuth and elevation observations. The results show that considering the Earth’s oblateness could improve the accuracy of the initial orbit determination by six times for a GRACE-like satellite, and by 60 times for a GPS-like satellite. Afterward, under the second scenario, the real observations of the SLR station were used. In view of increasing in the number of observation tests, by increasing the numbers of the observations from 3 to 15, the accuracy of initial orbit determination was improved from 1496 to 8 m using the SLR data for the GRACE-A satellite.

Attitude determination of the fault planes and slid movements occurring on these planes are among the topics of interest to geoscientists. Among the methods that have been introduced to determine the attitude of the fault planes so far, the use of geological tools for justifying the geometry of the faults with surface outcrops, and examining the changes of the stress field and the displacements appeared on the Earth’s surface can be mentioned. The slip rate is calculated using the displacement of the sedimentary rock layers relative to the displacement time and the simulation models.

In this research, a geometric method is presented to calculate the slip rate of Zagros faults. We consider each fault as a continuous set of fault fragments whose surface positions are known. Given that most of the Zagros faults are hidden, locating thefaults is carried out using the geologicalmap of Iran’s faults. The first issue in performing these calculationsisto determine the attitude of the fault planes in the Zagros seismogenic layer. The seismogenic layer is that part of the earth's crust whose deformation is elastic, and the major fractures caused by the earthquakes occur in this part. In order to determine the attitude of the fault’sfocal plane, we use the focal coordinates of the earthquakes occurringaround each fault segment. In performing these calculations, the focal locations of the earthquakes are transferred to the geodetic coordinate system and, the equation of the fault plane is calculated using the least squares method in the Cartesian coordinate system. One can obtain the azimuth of the strike of the planes relative to the astronomical north by calculating the coefficients of the fault planes. To determine the azimuth, we first obtain the unit vector of the strike line by cross product of the geodetic z-axis (normal vector of the horizontal plane) and the normal vector of the fault plane. The fault plane azimuth will then be the angle between the strike line vector and the north vector.The north vector is the vector which is determined by connecting the point located on the center of each faultfragment to the intersection point of the horizontal plane and thez-axis. Variation in dynamic mechanisms of thefaults in the region creates fractures with different directions on the ground. We obtain theslip angle (rake) of the fault from the difference of the fault direction and the direction of thesurface fracture and the type of thefault (strike-slip, dip slip and oblique).By calculating the slip angle, we now calculate the unit vector of the slip direction from the rotation ofthe strike line vector as much asthe rake angle.

In order to calculate the slip rate of each fault, we consider Zagros crust as an integrated object, which deforms uniformly by imposing the stress. Based on this assumption, we project the velocity vectors of the Zagros geodynamic network on the fault planes and calculate the slip rates using the slip direction vectors. It should be noted that the velocity vectors of the geodynamic network have been defined in the navigation coordinate system. According to the definition of the fault plane equations, it is necessary to transfer the velocity vectors to the geodetic coordinate system. The resulting slip rate is a parameter which is calculated for each fault fragmentindividually. Considering the effect of the systematic errors in the focalposition of theearthquakes, (including the error of the focal depth and the epicenter location), the slip ratesobtained for the fault fragmentsalways have systematic errors. Therefore, we define an average slip rate for each fault in order toreduce the error effect. In this study, velocity vectors of seventeen permanent stations of the Zagros geodynamic network provided by the National Cartographic Center (NCC) are used. The focal positions of the earthquakes are also published by the International Institute of Earthquake Engineering and Seismology (IIEES).  

The obtained results showed that the regions with high fault slip rate usually have dense earthquakes. In addition, the seismicity potential of any region can be found by comparing the slip rate of each fault and the density of its earthquakes in the region. According to the changes in the slip rate obtained in Zagros, faults in the western part of Zagros, especially in Ilam province, have low slip rates. However, the province is considered asone of the seismic areas of the state in terms of earthquake density.It means that most of the slip movements occurringon the faults of the western region have been accompanied by vibration.

This study was carried out to calibrate and validate the WOFOST model for winter wheat in Qazvin plain. Firstly, the model was calibrated based on the phenological data obtained from the field experiment during the year 2016-2017. Then the model was validated based on the four years field data. After that the model recalibrated in terms of physiological aspects using leaf area index, biomass and crop yield. The model simulated flowering and maturity dates with 11 and 4 days accuracy (RMSE). The simulation results showed an acceptable fitness with the observation data. After calibration, the root mean square errors (NRMSE) of simulated model were estimated to be 12.05, 11.1 and 15.4% for yield, biomass and leaf area index, respectively. Based on the obtained results, the model was estimated all the proposed parameters less than the field data (CRM<0). The highest model efficiency was obtained for leaf area index. After that the model efficiencies were 0.95 and 0.94 for crop yield and biomass, respectively. The lowest value for determination coefficient (CD) was obtained for biomass, showing the largest dispersion between simulation and measurements values.

Earth gravitational field interacts closely with a variety of natural phenomena. Therefore, studying and modeling the gravitational field and keeping tracks of its changes over time can make a huge contribution to the related geophysical and environmental researches. A well-proven method which is used in dynamic estimation of time varying state parameters is "Kalman filter". Kalman filter exploits dynamical properties of the states and noisy measurements to estimate the optimal state for every time epoch, in its filtering scheme. Smoothed properties of the involved dynamics, the accuracy of the noisy measurements, pay for noises in measurements and incompleteness of the interchangeable dynamics provide optimal estimates of state for the dynamic system. "Standard Kalman Filter" is performed to acquire the optimal solution when dealing with linear dynamics with white noises in both dynamics and measurements while a non-linear system cannot be dealt with in the same way. Various versions of Kalman filter have been introduced so far, including Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The former uses linearization to derive locally-linear dynamics while the latter is the approximation of the probability distribution of the states using sampling points of the state vector which are commonly referred to as "sigma points". Due to the linearization involved in EKF, the method does not give precise solutions when there are non-linearities of high orders while UKF solution is significantly better in the sense of precision, accuracy and optimality. In this paper, the 9-points Newton numerical differentiation is applied on the range values calculated from the relative state vector to reconstruct the so-called KBR range-rate observations and EKF is performed along with positions of observations to give an optimal estimate of the state vector at each time epoch. The calculated range-rates from both methods are then compared statistically. Then, UKF is used to fuse position and range-rate observations and to estimate state vector. Finally, the residuals of the reconstructed range-rate observations of three methods are compared statistically. The results show a relatively significant improvement in KBR observations reconstructions for UKF estimated states. To re-evaluate the superiority of UKF from the practicality point of view, the reconstructed range-rates are used to estimate the harmonics coefficients of geopotential model in a field recovery problem using "acceleration method" in a closed-loop simulation for EGM96. The degree-variances and differences in geoid heights are calculated and plotted. The results show a near 5 to 10-times fold improvement in gravitational field recovery using UKF results compared to the other two mentioned methods.

Evapotranspiration is one of the key components of the hydrological cycle and its quantification is essential to understand the processes such as vegetation phenological changes, environmental hazards such as floods and droughts, and, in general, the ecosystem water balance. The use of remote sensing methods based on surface energy balance has been increased to estimate evapotranspiration. The purpose of this study was to estimate the evapotranspiration of corn based on integration of Landsat 8 and MODIS satellite images using the SEBAL algorithm in in Mahidasht, Kermanshah province. Linear with Zero Intercept (LinZi) method was used to integrate satellite images. Also, actual evapotranspiration of corn in 15 farms in the study area was estimated based on ground data. The results of the SEBAL algorithm were compared with ground-based evapotranspiration using MAE, BIAS and RMSE indices. The results indicated that the combination of satellite images has led to an improvement in the accuracy of estimated evapotranspiration compared to Landsat 8 images. The mean absolute error of estimated evapotranspiration during the growth period was determined as 0.44 and 0.42 mm.day -1 respectively based on Landsat 8 and combined images. In general, the results of this study showed that the estimation of evapotranspiration using the SEBAL algorithm and based on the integration of satellite images with different spatial and temporal resolutions could have acceptable results.

Land consolidation strategy is very important for economic and agricultural purposes. This strategy can make optimum use of inputs. This study aims to investigate the effect of equipping, renovation and integration of the fields on the income and producing expenditure of the paddy fields, in Gilan Province. The statistical population of the research encompasses the integrated fields and it is consisted of 80,000 farmers of Gilan Province between years 1996 till 2009. The number of samples opted based on the Cochran formula and stratified random sampling method is equal to 384. The methodology of the research is analytic descriptive and the instrument of collecting information is a questionnaire. The collected data were analyzed using SPSS software and paired test (paired t-test). The findings indicate that the utilization of agricultural engineering techniques, mechanization and fields integration has had a positive and significant impact on reduction of expenditures and increase of income of exploiters, Therefore, this strategy should be considered in planning and policy making in the agricultural sector.

This research was carried out to study the adaptability and performance of four Eucalyptus spp. species in Bushehr province. For this purpose, one- year- old seedlings of four species, including Eucalyptus camaldulensis Dehnh, E. rubida H. Deane & Maiden, E. grandis W. Hill and E. saligna Sm. were planted under randomized complete blocks design, using 49 seedlings per plot at 5 × 5 m spacing at Shabankareh Research Station, Dashtestan, Bushehr province, in late February. All trees were irrigated, protected and enumerated each three months in the first year after plantation. Survived trees were counted and measured at the end of each year from second year on. Diameter at breast height (DBH), collar diameter, total height and crown diameter were measured to evaluate tree performance. Results showed that E. camaldulensis had the highest survival, height, DBH, crown area, standing stock volume and volume increment rates, followed by E. saligna. Application of Duncan test showed significant differences among species in terms of their survival and growth performance (α = 5%). Furthermore, E. rubida and E. grandis completely died at the end of first year of cultivation due to drought and high temperature stress in June and July. Based on these results, E. camaldulensis was suggested for plantation in similar sites in the south of Iran and further research was recommended.