multivariate regression

Analysis of statistical relationships and quantitative factors and parameters affecting the production of sediment and soil loss is one of the problems of the watershed. The purpose of this study is to model the relationship between sediment production rate using MPSIAC experimental model factors and observed sediment rate using geomorphic parameters and their relationship with sediment yield in the watershed. Rastamabad watershed of Ilam province was selected by a simple random method with four sub-watersheds identified and equipped with a hydrometric station from the southern basins of Ilam province. In this study, an experimental model was used to estimate the sediment yield. Flow and sediment data of four hydrometric stations and 12 meteorological stations from 1991 to 2020 for 30 years were prepared by the Regional Water Company and the General Meteorological Office of Ilam Province. Physiographic information of the sub-watersheds was calculated from topographic maps with a scale of 1.25000 and the geomorphic features of the sub-watershed were extracted from the digital elevation model. Using factor analysis and cluster analysis, influential factors and variables were identified and sub-domains were classified and divided into homogeneous regions. In order to investigate the correlation between independent and dependent variables, the data normality test was performed by Shapiro-Wilk and Kolmogorov-Smirnov tests in SPSS software. The statistical multiple regression method was used to analyze the relationship between experimental model factors and geomorphic variables with sediment yield of each watershed. The results showed that the sediment yield had a positive correlation with geological factors, land use, upland erosion, river erosion and topography of the watershed and was significant (P≤ 0.001). The amount of observed sediment had a positive correlation with slope, circulatory ratio, rainfall, topography, and area of the watershed and was significant (P≤ 0.001). In order to influence the factors and variables on the amount of sediment in the sub-watersheds, the method of principal component analysis and cluster analysis were used. The results showed that the land use explained 25.24% of the variance of all research variables. Finally, the three factors of land use, upland erosion, and geology, and two parameters of circulatory ratio and slope were explained at 86%. Of the variance of all research variables.

Awareness of water demand is of particular importance for its policy in urban management. Predicting water demand in the future will allow managers to take the necessary measures regarding sustainable water supply, given the constraints and crises ahead. The purpose of this study is to compare multivariate regression and ARIMA models to predict water demand in Mashhad. In this study, first, the main variables affecting water demand including rainfall, temperature, and population were determined and then the required data were collected from relevant organizations. After checking the accuracy and homogeneity of the data, water demand was estimated by two models of multivariate regression and ARIMA. The results showed that for data training, the determination coefficient and Nash coefficient were 0.81 and 0.77 respectively for the multivariate regression model and 0.77 and 0.73 for the ARIMA model, and the same coefficients for training data are respectively 0.86 and 0.87 for the multivariate regression model and 0.79 and 0.73 for the ARIMA model. Therefore, the multivariate regression model has a better performance in terms of prediction due to the high determination coefficient and being in the very good category of the Nash coefficient, and it can be used as an acceptable method for predicting water demand compared to the ARIMA model.

Focusing only on problems can lead to neglecting the strengths and capacities of a watershed. As an emerging approach, resilient thinking can be used to better understand watersheds as complex and dynamic systems between people and nature, and facilitate the transition to a more resilient watershed through efficient watershed management. Therefore, the present study is planned with the aim of conceptual modeling of watershed resilience using key dimensions of ecological, social, economic and infrastructure.

For this purpose, first the conceptualization of watershed resilience was done according to the prevailing conditions for the period 2016-2017. Then, the information and data of effective criteria in different dimensions were prepared and extracted, so that for the ecological dimension used from the watershed health index and for social, economic and infrastructural dimensions, 13, 8 and 13 criteria, respectively, were considered. Then, the mentioned criteria were standardized and their weight was determined according to their importance based on the principal component analysis method. Then, the criteria of different dimensions were first combined separately using the geometric mean method and the resilience of each dimension was obtained.

Then, through multivariate regression, the effect of each dimension on the resilience of the Sharand Watershed was investigated and finally, the resilience map of the Sharand Watershed was obtained.

According to the results of the present study, it can be acknowledged that watershed management in the face of risks requires a full understanding of the status and dynamics of social and ecological systems in response to stress. In this regard, using the approach of resilience and analysis of the affecting components in the watershed scale can be one of the effective ways to achieve integerated management of watersheds.

Climatic components are the vital factors influencing the dust event. It was believed that changes in climate parameters play a crucial role in the occurrence of dust events. Former studies have shown that temperature, rainfall, and wind speed contribute to poor vegetation, providing requirements of dust events. Long-term dust data indicated that climate has a profound effect on dust events and the occurrence of dust storms compared to the desertification process (Zhang and Anastasio, 2003). Monitoring Iran’s meteorological data indicates changes in precipitation and temperature in the last two decades (Rezaei and Ghasemieh, 2019). Alterations in the amount and pattern of rainfall and rising temperature can initiate the changes that cause natural hazards, including dust storms. Hence, in the present study, the trend of changes in climatic parameters in Khorasan Razavi province has been investigated. Note that the climatic parameters are consisting of rainfall, average temperature, wind speed, and frequency of erosive winds.

 According to the objectives of the present study, the monthly precipitation, temperature, and wind speed of seven synoptic stations including, Mashhad, Sabzevar, Sarakhs, Neishabour, Torbat Heydariyeh, Kashmar, and Quchan, were obtained from the meteorological organization of Khorasan Razavi from 1990 to 2016. Hourly dust codes also were extracted to detect the number of dusty days in the study period. The relationship between climatic parameters of precipitation, temperature, wind speed, and erosive wind frequency was inspected by the Pearson correlation coefficient for each station. Later, the trend of dust events and climatic parameters was analyzed using the Mann-Kendall trend test.

 Results of dust codes indicated the local origin for the highest frequency of dusty days. Even though Sarakhs and Mashhad stations had the highest dusty days, Quchan and Kashmar had the lowest one in the study region. The Mann- Kendall test pointed out an increase in temperature for all study stations except Neishabour. Meanwhile, the precipitation had a downward trend in the Mashhad, Kashmar, Sabzevar, Torbat Heydariyeh, and Sarakhs stations and an increasing trend in the Neishabour and Quchan. Raising the frequency of the erosive wind in Quchan, Kashmar, Sabzevar, Sarakhs, and Neishabour stations during the study period could also be stated. In most of the stations (except Mashhad and Neishabour) upward tendency in the number of dusty days is consistent with other researchers such as Mehrshahiand Nekounam (2009) in Sabzevar, Boochani, and Fazeli (2011) in Ilam, Shahsavani et al. (2011) in Khuzestan stated. Hence, the decreasing tendency of dusty days in Mashhad and Neishabour is also in line with Rafiei et al. (2015) in Yazd, Semnan, Kashan, Ramsar, and several other stations. The falling trend of wind speed in Mashhad station can be reflected as the main reason for the reduction of dust events. In addition, the rising tendency of precipitation and possible decline of drought in Neishabour can be considered as the main reasons for the decrease in the number of dusty days. To understand the effect of climatic factors on the occurrence and intensification of dust events, the relationship between the number of dusty days and climatic parameters was determined. The results of correlation analysis between dust events and climatic parameters showed that the temperature directly affects the dust events in all stations except Mashhad. The positive correlation between temperature and dust events was stated in other studies (Bahrami et al., 2013; Amgalan et al., 2017). The correlation coefficient between dust event and precipitation showed a negative relationship between them which was significant at 95% level in Sarakhs station. Other studies (Ali et al., 2016; Broomandi et al., 2017; Amgalan et al., 2017) have also indicated the inverse effect of precipitation on dust events, which is following the findings of this study. The results of correlation analysis between the number of dusty days and wind speed and the frequency of erosive wind showed that the enormous influence on dust events is wind speed. While, at Neishabour station, there is a significant negative relationship between dust and wind speed. Indeed, the inverse relationship between dust and wind speed indicates that with increasing wind speed, the dust emission does not increase and another factor reduces the effect of wind speed. As Kimura (2012) reported the impact of vegetation on reducing the wind speed impact on the dust occurrence.

 It was proven that the number of dusty days is in line with the variations of the climatic parameters. The decrease in rainfall, increase in temperature, and wind speed has been accompanied by an increase in dusty days in the region. In general, correlation analysis of dusty days and climatic parameters in Khorasan Razavi province indicated that wind speed and the frequency of erosive wind have considerable influence on dust events. However, the effect of wind speed on dust events at the Neishabour station reflected the significance of vegetation cover on reducing the transfer of soil particles.

Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, to determine the factors affecting sedimentation, the Urmia Lake watershed was selected as the study basin. After identifying 30 characteristics affecting the sedimentation of sub-basins of the study area, including hydrological, physiographic, geomorphological, geological and soil characteristics, climate, land use and vegetation as independent variables, the amount of sediment produced in each sub-basin. Was identified as a dependent variable. Using factor analysis, principal component analysis (PCA), cluster analysis and stepwise multivariate regression between selected independent variables and dependent variable using SPSS software Statistical relationship was obtained between sedimentation of sub-basins and watershed characteristics. According to the selected regression model, it is determined that the amount of sediment in the watershed of Lake Urmia to five factors of agricultural land area (rainfed, irrigated and orchards), the area of sub-basins, the total area of erosion and Quaternary structures, average discharge The annual and basin form factor depends on the fact that these five factors control 89% of the sediment production changes in the selected sub-basins, which is significant at the 5% confidence level. In general, the factors affecting erosion and sedimentation of the Urmia Lake watershed can be divided into three groups: human factors and land use change, geology and physiography.

Primary production and the contribution of each of the life forms of rangeland ecosystems are key features. The purpose of this study was to determine the effect of rainfall and temperature changes on the amount of total production and life forms: grasses, forbs and shrubs in Sabalan,s rangelands. For determining of annual primary production (Full bloom stage), different species using one square meter plots by cutting and weighting method in different elevation was harvested in 6 plant types (totally 216 plots). Rainfall classes and temperature parameters were determined using the data of weather stations and derived relevant gradient. Then, climatic parameters for plots was derived by considering the position of sampling plots. Correlation between the life forms and total production with rainfall and temperature parameters were analyzed using multivariate regression method in SPSS16 software. Results show that, climatic parameters with the life forms production of grasses (P<0.01), forbs (P<0.01) and shrubs (P<0.01) have significant relationship, however there was no significant relationship between total production and selected climatic factors. Moreover, results show that the production of grasses are more affected by climatic factors in comparison with the other two life forms and have significant differences in different classes. Additionally, forbs production is more than shrubs affected by the selected climatic factors. The result of study can be used for the calculate primary production in mountain region, supply-demand balance of forage, biomass, health Ecosystem, Ecological balance and carbon sequestration.

Most of the studies on dust storms are concentrated in the western-eastern provinces of the country and less research has been done in other parts of Iran. In this research, some of the southern plains of Alborz; which have the potential to produce dust were selected. Daily data of 18 meteorological stations with long-term statistical period from 2008 to 2017 and to calculate the indicators of agricultural drought, MOD13Q1 and MOD11A1 products were used in the same period. Standardized precipitation index and standardized precipitation-evapotranspiration index were calculated and from the dust hourly meteorological codes, the experimental dust storm index was obtained. Then the relationships between climatic indices, vegetation and experimental index of dust storm were investigated using Pearson correlation and multivariate regression statistical method. The highest frequency of DSI index during 10 years is related to Imam Khomeini station with a value of 30.5 and the lowest is Taleghan station with 0.05. The Year-on-year changes in the dust index show that in the early years, the central and southeastern parts of the region were affected by dust storms, and sudden decrease had a significant effect on the results and reduced the long-term trend. The highest and the lowest correlation is respectively wind speed and Temperatur .Regarding the results of monthly multivariate regression, are somewhat variable in different months of the year, so that the effect of climatic elements and vegetation changes in different months, in general wind speed, TCI and SPEI have the most important effect on increasing

Soil temperature is an important and influential parameter on plant growth that is not measured regularly and continuously at all weather stations. Thus, soil temperature data is lacking. Soil temperature differs in depth and is affected by ambient. The aim of this study is modeling soil temperature in the different depths (5, 40, 20, 30, 50 and 100 cm) and clay- sandy texture using meteorological factors, multivariate regression and artificial neural network in the Bandar Abbas synoptic station during 1993-2017. Result showed that the air temperature, pan evaporation and dew point have the highest correlation coefficient with soil temperature. The mean absolute error (MAE) is 1.09-1.88°C (from 10 to 100 cm of soil depth) in the multivariate regression model while it is 1.17-1.85 °C in the artificial neural networks. Thus, multivariate regression model is proposed due to the simplicity and the lack of significant difference with artificial neural network model. This model can be used in similar regions to predict soil temperature in different depth.

Flood is a kind of natural disaster which causes financial damages and fatality for people. Every year, especially in areas like Maroon river basin which have changes in precipitation and temperatures, along with frequent and severe floods. This study aimed to identify the climatic parameters on flood area can be efficiently artificial neural network, better methods applied in anticipation of this event. The method used in this study to predict the process, multilayer perceptron neural network and radial basis that these two neural networks with multiple regression results were compared. Therefore, climatic daily data in 16 years cycle from four stations: Idnak, Dogonbadan, Dehdasht and yasouj (23 September 1994-22 September 2009) are used. By study of correlations between climatic parameters and discharge of Maroon river, effective parameters on flood are determined and multiple regression is used because of determination of effective entrance on flood and comparing the results with the neural network. Study of the results shows that multilayer Perceptron (MLP) along with training algorithm after flowing the error have 0.73 correlation in training process and in test process is 0.68 and also measure of NRMSE in training process is 0.57 and in test process is 0.66 that known as the best model for predicting storm water. Comparing the results of regression and neural network shows that neural network have a higher correlation than the regression, thus in neural network actual data and predicted data have more conformity than accomplished regression model.

Achieving the detailed information about flood and sediment, due to practical problems and the lack of hydrometric and sediment gaging stations is very costly and difficult. Therefore, the present research is sought to determine the significant relationships between the physical parameters of Garmab watershed and various components of flood and sediment. For this purpose, all input information such as area and perimeter of watershed, the watershed maximum, minimum and average altitudes of sea level, main stream length,main stream slope, watershed slope, form factor and drainage density variables, annual erosion and specia erosion (m3/km2/y) and flood with the return periods of 2, 5, 10, 25, 50 and 100 years (m3/s) collected. Then, the relationship between 8 estimated parameters of flood and sediment and 10 physical parameters of watershed performed using the multivariate regression analysis and neural network methods. The study of the effective inputs in the multivariate regression method showed that the area and slope variables of watershed are the effective parameters in flood production and the area, minimum and average height of watershed and the main stream length variables are the effective parameters in the sediment production at Garmab watershed. Also the radial basis function network (RBF) for the predicted values of specific erosion and multi-layer perceptron network (MLP) for the predicted values of the estimated annual erosion, respectively, with the maximum values in the coefficient of determination equal to 0.98 and 0.99 and the minimum values in the root mean squared error equal to 0.16 and 253.56, selected as the most efficient models. But for the predicted values of flood with the return periods of 2, 5, 10, 25, 50 and 100 years, the multivariate regression model according to the maximum value of the coefficient of determination, the minimum values in the root mean squared error and the absolute relative error, selected as the best model.

Landslides are major natural hazards which not only cause damages to human life but also provide economic losses on infrastructures. In order to determination of the most important method of estimation recognizing appropriate method to estimate landslide, in this research, the efficiency of two methods of landslide hazard zonation including methods of Artificial Neural Network and Multivariate regression were compared. Therefore, in this research, first, landslide inventory map was obtained using aerial photos interpretation, satellite images processing, geology maps review and field surveying. Also, the 9 important effective factors are in occurrence of landslide including lithology, land use, slope angle, slope aspect, elevation, precipitation, distance to fault, distance to road, density of drainage were determined using inspect of field and literature review. After producing of layers and weighting to effective factors using inventory map, landslide hazard zonation was made by Artificial Neural Network and Multivariate regression models. From 200 landslides identified, 140 (≈70%) locations were used for the landslide susceptibility maps, while the remaining 60 (≈30%) cases were used for the model validation. The quality sum (Qs) and precision (P) indices for Artificial Neural Network model are 0.15, 0.08 and for Multivariate regression model are 0.14, 0.05 respectively. This results show that artificial Neural Network is the better model in landslide hazard zonation in this area, therefore an accurate landslide hazard zonation map can be prepared by selecting and applying the proper method.

Accurate and reliable simulation and prediction of the groundwater level variation is significant and essential in water resources management of a basin. Models such as ANNs and Support Vector Regression (SVR) have proved to be effective in modeling nonlinear function with a greater degree of accuracy. In this respect, an attempt is made to predict monthly groundwater level fluctuations using Multivariate Linear Regression, Multi-Layer Perceptron neural network models and two SVRs with RBF and linear function. In the present study, monthly data (from 2000 to 2010) of 18 observational wells in Shahrekord Plain were used for simulating and predicting the groundwater level. Regarding to NS efficiency and RMSE criteria, MLP model in 56% and SVR in 44%, have the best performance in comparison with other models. For an instance, in well No. 1, SVR-RBF using input parameters of groundwater level, temperature, evaporation and precipitation is superior to other models. General efficiency of MLP, SVR-RBF, and SVR-Linear for NS criteria is 0.703, 0.656 and 0.655, respectively; and for RMSE criteria is 0.857, 0.905 and 0.914 meter, respectively. Results indicate that MLP and SVR models give better accuracy in predicting groundwater levels in the study area when compared to the linear model.

Identification of factors affecting on existing of landslide as well as its zonation in the given watershed is one of the basic tools for landslide control and selection of approach and effective solution as well. In this study landslide hazard mapping provided using previous landslide distribution and multivariate regression model in GIS environment in Aghmashhad. With reviewing previous resources and studying of Aghmashahd watershed, the eight factors of altitude, slope, aspect, geology, soil, land use, distance from river and distance from road were selected. With a field observation of the whole watershed, the local information and GPS were listed and landslide preparation map was prepared with stepwise multivariate regression in the statistical software SPSS. The distance from the river factor was selected as major factor affecting the landslide. The benefit of this model is to assess the expansive areas in short time. Research result showed that there is a significant correlation at the 0.01 level (P< 0.01) between potential hazard values, classes and current land slide area.

Measurement and monitoring of vegetation cover fraction is very important in many environmental studies. Although remote sensing techniques have shown successful to estimate the parameter, but still remote sensed measurements of it encounter to special problems in desert areas. In this research has tried, the relationship between reflections in the different bands of LISSIII and TM sensors and percentage of vegetation cover be investigated using field measurements of vegetation cover fraction in a desert area. Accordance with the findings of this research, if we consider the relationship linear, only correlation between the percentage of vegetation and the near-infrared band has shown significant at the 0.05 level (2-tailed). While if we consider it nonlinear, correlation between reflections of all bands and the percentage of vegetation cover have shown significant at the 0.01 level (2-tailed). Also according to the results was determined using a linear multivariate regression equation and using all the bands, it is possible to make the relationship significant at all levels and to improve accuracy of estimations of the model. Even was observed with the change in the type of used regression model and covert it to a non-linear multivariate regression model, it is possible to improve the correlation coefficient and the coefficient of determination between the observed and estimated vegetation cover percentage significantly, In a way that correlation coefficient and the coefficient of determination between the observed and estimated vegetation cover percentage at best condition is calculated 0.917 and 0.841 respectively. Based on the results obtained in this study to increase the accuracy of modeling the nonlinear relationship between percent vegetation cover and the band satellite reflections using artificial neural network is the best way. In this research using artificial neural networks increase the accuracy of modeling significantly, so that the the coefficient of determination was passed 0.9 at best. Also based on neural network modeling hundred repetitions of this research can be refer to a neural network with radial function has better accuracy of modeling than multi-layered neural network to determine the percentage of vegetation using satellite data in drylands.

Identifying areas with different potential to landslide occurrence is one of the primitive actions in natural resources management. In view of importance of recognizing appropriate method to estimate landslide, in this research, the efficiency of two methods of landslide hazard zonation including methods of Mora and Varson and Multivariate regression were compared. In this research, first, layer of landslide was created in Arc GIS using aerial photographs with scales of 1:20000 and 1:50000, field monitoring and GPS. To zone landslide hazard in research area using methods of Mora and Varson and Multivariate regression, efficient layers for each method were created. After creating and weighting each layer, research area zonation to be done. Assessment of results of different models using quality sum index (Qs) and correlation coefficient shows that Multivariate regression is the best model in zonation of research area from the view point of the landslide hazard. According to the Multivariate regression, research area was separated to six classes of landslide hazard. Therefore we can manage the basins and fighting with landslide hazard by less cost and time By selecting the best method.

One of the most appropriate approaches for flood forecasting is using peak discharge data of hydrometric stations in each region. However, lack of such stations or short duration of data in most parts of the country, make it necessary to use some alternative methods in order to estimate the flood discharge properly. One of these approaches is regional flood analysis method that in a region using observation discharge data in separate points, it calculates relevant regional flood models. These approaches give us possibility at a region without gauging station with similar and homogenous hydrological condition to estimate flood discharge for different return periods with acceptable accuracy. In this research three methods of regional flood analysis including index flood, multivariate regression and hybrid method were considered in 20 watersheds of central Alborz region. After taking into account, the hypothesis and limitations of each method, the results were compared with observed flood discharges using RMSE and MBE. Considering the hypothesis and validation of multiple regression model indicated it is not appropriate. Finally Index Flood method in return periods of 2, 5 and 10 years and hybrid method in return periods of 50 and 100 years proved higher accuracy in the whole region and no difference between these two methods in return period of 25 were shown.