جستجوی مقالات مرتبط با کلیدواژه "پیش بینی" در نشریات گروه "آب و خاک"

In recent years, the use of artificial intelligence methods, such as artificial neural network models, have become increasingly prevalent in simulating complex natural phenomena, including daily streamflow. The streamflow directly correlates with flood occurrences, and mitigating financial and human losses due to floods is crucial. Accurate streamflow simulation is essential for water resource management and river management. Consequently, in hydrology, deep learning methods have emerged as novel tools to address the longstanding challenge of daily streamflow modeling and are widely used in simulations.Advancements in streamflow modeling with Artificial Intelligence (AI): In recent years, the field of hydrology has witnessed a significant shift toward leveraging AI techniques for streamflow modeling. Among these methods, artificial neural network (ANN) models have gained prominence due to their ability to capture complex relationships within hydrological systems. Streamflow, which represents the flow of water in rivers and streams, is a critical variable for understanding water availability, flood risk, and ecosystem health. By accurately simulating streamflow, researchers and water resource managers can make informed decisions regarding water allocation, flood preparedness, and environmental conservation. Hydrological processes are inherently nonlinear and influenced by various factors such as precipitation, temperature, land cover, and soil properties. Traditional hydrological models often struggle to capture these complexities. However, deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions. These models can learn intricate patterns from historical streamflow data, adapt to changing conditions, and provide accurate predictions. As a result, they have become indispensable tools for addressing the longstanding challenge of daily streamflow modeling. Researchers continue to explore novel architectures, data augmentation techniques, and hybrid approaches to enhance the performance and robustness of AI-based streamflow simulations. In summary, the integration of deep learning methods into hydrological research has revolutionized streamflow modeling, enabling more accurate predictions and informed decision-making in water management and flood risk assessment.

In this study, we focused on selecting an appropriate input scenario for deep learning models and simulate daily streamflow on the Kashkan River using LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) deep learning methods. Prior to this, deep learning modeling with the GRU approach using native streamflow measurements had not been performed for Kashkan river. The study area is a flood-prone and mountainous region, specifically the western part of Iran, where a hydrological station with a history of flood events is situated on the Kashkan River. We employ four approaches for handling outliers (Mahalanobis, critical interval removal, Z-Score, and no removal) and four different preprocessing techniques for input data to train two models: LSTM and GRU. Ultimately, eight distinct models are generated and validated against historical data. The input features include regional average precipitation, normalized vegetation cover index, surface soil moisture, groundwater flow, and the Kashkan River’s own flow at the hydrological station, with the best features selected using statistical correlation control.

The results demonstrate that among the deep learning models generated with a 10-day time step, the model with the least error and consistent low error retention in error metrics is observed. Furthermore, the best performance is achieved using different approaches, in the following order: the GRU model with Z-Score-corrected inputs, followed by the Mahalanobis removal approach with average RMSE (Root Mean Square Error) and KGE (Kling-Gupta Efficiency) values of 5.41 and 0.99, respectively, and the critical interval removal approach with RMSE of 6.23 and KGE of 0.7.The results showed that among the deep learning models produced with a time step of 10 days in the model, the lowest amount of error and the persistence of low error can be seen in the error statistics, and among the different approaches used, the best performance is the GRU model with input modified by Z-Score elimination of outlier method, Mahalanobis elimination method with average RMSE and KGE values of 5.41, 0.99, 6.23, and 0.7 in the training phase and 8.17, 0.79, 4.21, and 0.81 in the validation phase and 5.01, 0.68, and 7.21 and 0.52 are in the testing phase. The obtained results do not reject the LSTM method in simulating the river flow, but state that the listed scenarios, especially in the GRU method, have a higher power in dealing with the data and recognizing the complex pattern of daily river flow, taking into account the limitation in use They have seven years of regular daily data, and future research will show how the behavior of GRU and LSTM models will differ if data with higher convergence is used.

GRU in future studies can make difference by enhanced flood forecasting accuracy, efficient computation and real-time applications, integration with lag time preprocessing, adaptability to changing climate and urbanization. Future studies will be on data driven method in flood prone areas. There remains ample room for future research and innovation. Here are some directions for further exploration: hydrological data fusion, spatially explicit models, uncertainty quantification, climate change resilience.

Dust storms pose significant environmental and economic challenges, particularly in arid regions like Sistan-Baluchestan Province, Iran. This study aims to compare the performance of individual models (GRNN and SVM) with a triple hybrid model (GRNN-SVM-LSTM) for forecasting the frequency of dust storm days (FDSD). Using hourly dust data from eight SYNOP codes of the World Meteorological Organization across five synoptic stations, spanning a 40-year period (1980–2020), the models were evaluated based on key performance metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NS). The triple hybrid model outperformed all other approaches, achieving the highest predictive accuracy in seasonal combinations 1 and 2. The SVM model ranked second, while the GRNN model performed relatively better in combinations 1 and 2 compared to combination 4. Overall, the GRNN-SVM-LSTM model demonstrated superior predictive performance for FDSD, with RMSE = 0.523–0.501, R = 0.999–0.989, MAE = 0.441–0.421, and NS = 0.907–0.893. These findings highlight the potential of the proposed model for improving dust storm forecasting and developing early warning systems.

Groundwater resources are an important factor in managing and maintaining water that is used for drinking water, irrigation and other purposes. Groundwater level forecasting is very important for assessing total water resources and their allocation, contributing to water sustainability and drought mitigation. Sometimes, due to the presence of obstacles such as unfavorable weather conditions, blocked roads, or lack of equipment and people, measurements are not carried out for months. On the other hand, accurate and abundant groundwater level data helps to predict various consequences related to groundwater management and ecosystem health. Nevertheless, completing the missing data and improving them by interpolation method helps effectively in predicting the stability level by deep learning method. . In this study, the Azarshahr aquifer, which has recently faced a significant drop in the underground water level, was examined monthly from 1397 to 1400. Also, in order to complete the data that was not measured for any reason, kriging interpolation methods and M5P algorithm were used. By analyzing each method, the M5P method with the minimum root mean square error of 1.83 meters and correlation coefficient of 0.975 was the best. It had the function. On the other hand, in order to predict the underground water level, the data was divided into 70 and 30 calibration and accuracy measurements, and the deep learning (DL) method was used, which was acceptable with an error of 1.408 meters and an accuracy of 88%. And it can be used in future research for better management of water resources.

Soil temperature is one of the important factors in agriculture and hydrology, and its accurate measurement is very important to ensure the optimal growth and development of the plants. Soil temperature is a factor that affects many processes such as seed germination, soil moisture level, aeration, nitrification and availability of plant nutrients. Because the soil temperature data is measured in some synoptic stations, most of the data have limitations or are incomplete. However, choosing the best method to estimate soil temperature with other available meteorological data is an efficient approach in many fields. Soil temperature depends on several factors including color, slope, vegetation, density, humidity and amount of sunlight. Currently, some physical models are available that are intrinsically related to the state of soil heat flow and energy balance in underlying soils to estimate soil temperature. The importance of soil temperature in agricultural sciences and hydrology, on the one hand, and the existence of many difficulties in recording this vital parameter, have led researchers to seek a relationship between soil temperature and other parameters in order to be able to estimate soil temperature with optimal accuracy.

In this research, daily soil temperature values were collected during the time period of 1990-2022 in Ramsar, Arak and Shiraz stations. On the other hand, the parameters of minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tm), maximum relative humidity (Umax), minimum relative humidity (Umin), average relative humidity (Um), average wind speed (FFM) and Sunshine hours (SSHN) was considered as the input parameters and soil temperature (T-soil) as the target parameter. It is worth mentioning that the way of choosing different input compounds to estimate the value of soil temperature in the studied models is based on having a higher correlation with soil temperature based on the thermal map. Moreover, the ability of data-driven models of Gaussian process regression (GPR), support vector regression (SVR), M5P algorithm, linear regression (LR), and multilayer perceptron (MLP) neural network in estimating soil temperature was evaluated using different statistical parameters of correlation coefficient (R), root mean square error (RMSE), Nash Sutcliffe coefficient (NS), average absolute value of percentage error (MAPE) and Wilmot agreement index (WI).

The evaluation of five GPR, SVR, M5P, LR and MLP models for three stations of Arak, Ramsar and Shiraz shows that the 8th M5P scenario and the 8th LR scenario with lower root mean square error respectively (0.899 and 0.889) for Ramsar station, (0.958 and 0.949) for Arak station and (0.966 and 0.953) for Shiraz station have better performance than other studied models. Also, the evaluation of the impact of the input parameters in creating the scenario for the models shows that the parameters of relative humidity and air temperature had more important role than other input parameters. So that by adding parameters of relative humidity and air temperature, the accuracy of the model has increased. Therefore, these parameters are among the most key and important parameters of soil temperature.

The analysis and evaluation of soil characteristics has an important impact in the fields of hydrology, agriculture and climate. On the other hand, soil temperature has a direct relationship with the amount of moisture available to the plant, so that an increase in soil temperature can increase the transpiration rate of plants, and as a result, soil moisture decreases. Soil temperature is also an essential factor in agriculture because it determines whether plants can grow, and controls soil chemistry and biology and atmosphere-land gas exchange. Therefore, predicting soil temperature is very important for successful crop management and yield optimization. So, In this research, five data-driven methods of GPR, SVR, M5P, LR and MLP were used to predict soil temperature in Arak, Ramsar and Shiraz stations during the time period of 1990-2022. The obtained results were compared using statistical parameters and it was concluded that the 8th M5P scenario and the 8th LR scenario have shown the best performance in three stations with the lowest error compared to all scenarios. Therefore, the application of the mentioned models to predict the soil temperature has proper accuracy and is recommended for management and evaluation in terms of environmental and civil aspects.

Daily reference evapotranspiration prediction is a decisive and useful tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. The use of hybrid models with the help of climatic factors is an effective method in the daily reference evapotranspiration forecasting process. Therefore, in this study, the ability of the support vector regression model (SVR) and the combined model of support vector regression with the fruit fly algorithm (SVR-FOA) in estimating daily reference evapotranspiration in Ahvaz station during the period of 2000-2022 using four statistical criteria was evaluated. The inputs used included parameters of average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours. The sensitivity analysis of the input parameters using Pearson's correlation coefficient also showed that among the input parameters, the parameters of sunshine hours and relative humidity were effective components in the prediction of evapotranspiration, thus reducing the error in all models. The obtained results showed that the sixth scenario of the SVR-FOA model provided the best performance with the lowest error (1.24 mm/day) compared to all models. Among the scenarios of the SVR model, the third scenario of the SVR model showed the lowest error (1.45 mm/day) compared to other SVR combinations. The results of this research showed that the sixth scenario of the SVR-FOA model had the best performance, and the fruit fly hybrid algorithm improved the performance of the support vector regression in estimating daily reference evapotranspiration.

The industrialization of societies has led to a rise in the emission of greenhouse gases in recent years. Such a rise causes global warming and, in turn, affects other components of the climate system, leading to certain climatic events that are fundamentally different from the natural course of climatic events, called climate change . According to the IPCC in 2007, the process of climate change and global warming have a significant impact on various systems such as water resources, agriculture, drinking, and industry such that the continuous increase in greenhouse gas emissions will intensify these effects, causing a warmer climate. Climate change represents the changes in precipitation pattern, snowmelt, and access to drinking water and agriculture . Groundwater resources are susceptible to both climate changes through (A) direct interaction with surface water sources such as rivers and lakes and (B) indirect interaction via the feeding process . Climate change indirectly affects the discharge and storage of groundwater by changing the nutritional conditions induced by rainfall and runoff; therefore, identifying and analyzing effective parameters such as climatic parameters can greatly help predict serious hazards threatening groundwater resources such as subsidence and drought. Moreover, considering that the relationship between climatic parameters and groundwater resources is complex and non-linear, the application of artificial intelligence models including modern hybrid models is a good solution to solving these problems. Therefore, the objective of this study is to analyze and predict climatic parameters using general atmospheric circulation models in coming years and groundwater level forecasting in Khorramabad plain using integrated vector regression model with the help of Wavelet Transform (WT) and modern optimization algorithms such as creative rifle and grey wolf optimizer. The basis of climatic parameters is the groundwater level and abstraction from the aquifer.

it is necessary to provide a solution and make proper forecasting of groundwater resources in order to prevent subsidence and drought phenomena around the world and in Iran. Therefore, in Iran, Khorramabad plain located in Lorestan province, which is very important in terms of drinking and agriculture whose products in this plain feed on groundwater for growth and development, has been subjected to illegal harvesting and digging of illegal wells. The level of groundwater resources has declined sharply in recent years. Therefore, groundwater level changes are more than necessary for forecasting and management measures to improve it.In this study, in order to simulate and predict the groundwater level of Khorramabad plain, the climatic parameters affecting the groundwater level were studied first according to the general circulation models of the atmosphere. In addition, the support vector regression model approach was used to predict the surface water level, assuming that the amount of groundwater abstraction from the Khorramabad plain was consistent with that in the previous statistical period. Since the SVR model is subject to errors according to recent findings, the strategy of optimizing the adjustment parameters using meta-heuristic algorithms was adopted to reduce the model error. To facilitate the groundwater level simulation, new meta-heuristic algorithms with WSVR support vector wave regression model, having acceptable performance according to several studies, were used. This approach can ensure taking an effective step in simulating and predicting groundwater levels.Based on the structure of the SVR, the most basic step is to determine the tuning parameters. The coefficients of these parameters are usually determined through trial and error in SVR. Many factors affect the viability of trial and error and the accuracy ofmodel prediction. Given the nature of trial and error, the predictive power may be generally reduced. Numerous solutions have been proposed by various researchers to address this fundamental weakness. One of these solutions adopted by researchers is to calculate the coefficients of parameter adjustment and optimize these coefficients by using meta-heuristic algorithms. A meta-initiative is the general framework of an algorithm that can provide solutions to the same problem with minor variations of various problems. There are many meta-heuristic algorithms such as Genetic Algorithm, Forbidden Search Simulation, Ant Society, Particle Swarm, Differential Evolution, Harmony Search, Artificial Bee Society, Firefly, Cuckoo or Coco, Frog Leap, Invasive Weed, and Competition, in addition to spirals, pollen, gray wolves, social spiders, ant lions, whales, locusts, and so on. Therefore, to optimize the adjustment coefficients in SVR, this study has employed new optimization algorithms including creative rifle and black widow spider, presented in 2021, for the first time in hydrology and water issues.

In this study, upon using climate change modeling, meteorological parameters (temperature and precipitation) for the years 2021-2040 were predicted and, then, by using ultra-exploratory hybrid models such as WSVR, AIG-SVR, and BWO-SVR, the groundwater level decline in Khorramabad plain located in Iran was predicted with the help of rainfall, temperature and harvest parameters associated with the four piezometric aquifers (Sarab Pardeh, Sali, Pol baba, and Naservand). Evaluation of LARS-WG model using baseline data (1990-2014) demonstrated that this model had a good performance in predicting meteorological parameters. The simulated temperature in all of the climatic models (EC-EARTH, GFDL-CM3, HADGEM2, MIROC4, and MPI-ESM) under the RCP85 emission scenario in the future time period (2021-2040) experienced an increase, compared to the base period in all months, while the average rainfall did not follow a definite trend. According to the statistical time periods of 2000-2020, WSVR, AIG-SVR, and BWO-SVR hybrid models in the combined structure including all input parameters had better performance due to higher memory, and WSVR model was more accurate with less error due to

Therefore, due to the decline in groundwater levels over the next 20 years, it is recommended that smart meters be installed on all agricultural and industrial wells and the drilling of illegal wells be prevented. In addition, the owners of agricultural wells need to be informed and notified of the dangers associated with the cultivation of hydrophilic crops. This strategy is useful and facilitates the development and implementation of groundwater management strategies and it is a step forward towards management decisions to improve the quantity of groundwater resources.

The constant need to increase agricultural production, along with more and more frequent drought events in the country, requires a more accurate assessment of irrigation needs and thus a more accurate estimate of actual evapotranspiration. Prediction of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Irrigated agriculture is expected to produce more crops with less water consumption in the future. Therefore, accurate forecasting of water demand along with sustainable management and more efficient methods to meet the growing demand under scarce water resources is necessary. The models used to predict evapotranspiration should be used in different regions with different climates to evaluate their performance. Therefore, in this research, tree models and Hargreaves were used in Yazd and West Azerbaijan provinces, which have different climates, in order to evaluate the performance of the models used.

In recent years, water management issues have been addressed using models derived from artificial intelligence research. In recent years, water management issues have been addressed using models obtained from multiple types of research. The use of combined models has made significant progress in recent years. combined models are able to perform processing in a short period of time and at the same time with high accuracy. Using these models, the main challenging aspects are represented by the selection of the best possible algorithm, the selection of suitable representative variables and the availability of suitable data sets. Therefore, in this study, the ability of tree models (M5P and RF) with Hargreaves model (Hs) in estimating daily evapotranspiration in Urmia and Yazd stations during the period of 2000-2021. The noteworthy point is that in the combined tree-Hargreaves model, the used tree models were used as input to the Hargreaves model. The combined model has been used for the first time in this research and the use of this model can predict daily evapotranspiration as well as possible.

The results of the model are performed using 5 evaluation criteria of Coefficient of determination, Root mean square error, Nash-Sutcliffe coefficient, and Wilmot’s index of agreement. In all the used models, the best scenario was the model whose input included parameters of minimum temperature, maximum temperature, relative humidity, wind speed, and sunshine hours. Comparison and evaluation of standalone tree models showed that in the Urmia station two models RF-5 and M5P-5 had less error (0.4 and 0.38-mm day-1, respectively) than other standalone models. Similarly, in the Yazd station, RF-5 and M5P-5 models have higher accuracy (0.36 and 0.35 mm day-1(, respectively) than other standalone models. For combined models, the obtained results showed that the fifth scenario of the M5P-Hs model provided the best performance in Urmia and Yazd stations with the lowest error (0.33 and 0.24 mm day-1) respectively. It was also concluded that the fifth scenario of the RF-Hs model in Urmia and Yazd stations had a lower error (0.36 and 0.26 mm day-1) than other models, respectively. Finally, tree models have increased the accuracy of the Hargreaves model in this research.

Finally, the RF, M5P, RF-Hs and M5P-Hs models were able to predict daily evapotranspiration values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. The results of this research showed that the combination of tree models with Hargreaves model is able to predict daily evapotranspiration values with high accuracy compared to individual models. The results of this research showed that the wind speed parameter is one of the most important meteorological parameters needed in estimating daily evapotranspiration, so adding this parameter results in the highest accuracy in all models. Also, due to the important role of wind speed in predicting daily evapotranspiration values and the unavailability of the maximum wind speed parameter in this research, it is recommended to use the maximum wind speed parameter as one of the model inputs for further studies.

Indiscriminate use of water resources and the occurrence of drought in recent years have caused many problems in the country's water resources. The increasing shortage of water resources and high irrigation costs require developing new irrigation methods for optimal water consumption, which can minimize the amount of water used to produce yields. Evapotranspiration is one of the most important parameters needed to estimate the water balance in any ecosystem. Evapotranspiration is an essential parameter in the hydrological cycle process in natural ecosystems, which links the water and energy balance of the earth's surface with the atmosphere. Reference evapotranspiration (ET0) plays an important role in the availability of water resources and stimulating the hydrological effect of climate change. Accurate estimation of ET0 is necessary for forecasting climate changes, predicting and monitoring droughts, assessing the lack of availability of water resources, assessing crop water needs, and planning irrigation. FAO's Penman-Monteith method is known as a standard reference method for estimating ET0. However, this model and, in general, water balance-based assessment methods require accurate and long-term meteorological data, which are not always and everywhere available. Therefore, alternative methods for predicting ET0 at different temporal and spatial scales should be developed, which are easily applied and require fewer input data without compromising the estimation accuracy. Also, due to the high rate of evapotranspiration in the coastal and central stations of the country, so far, few studies have predicted the ET0 parameter. Therefore, this study was carried out to predict daily reference evapotranspiration in Isfahan and Astara stations. The current study is forecasting daily reference evapotranspiration in two stations of Astara and Isfahan using Gaussian Process Regression (GPR), Support Vector Regression (SVR), M5P tree model, and M5Rules linear regression. For this purpose, the daily meteorological data of the stations including average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours during the period of 1990-2021 as inputs to the models was used. Also, to evaluate the effectiveness of the models, the evaluation criteria of determination coefficient (R2), root mean square error (RMSE), Nash-Sutcliffe coefficient (NS), and Wilmott's index of agreement (WI) were used. The evaluation of the results of different scenarios of the GPR model in Astara station showed that the fifth scenario was recognized as the best scenario of this model due to having a lower error value (RMSE=1.52 mm day-1). For the M5Rules model, the fifth scenario has performed better than the other scenarios of the M5Rules model due to having fewer inputs and similar errors compared to the sixth to eighth scenarios (RMSE=1.42 mm day-1). In the M5P model, the fifth scenario has a higher accuracy than the other scenarios due to having a lower error value (RMSE=1.42 mm day-1). For the SVR model, the sixth scenario with the least error (RMSE=1.58 mm day-1) was selected as the best scenario compared to other scenarios of the SVR model. For the Isfahan station, for the GPR model, the fifth scenario has performed better than the other scenarios due to having fewer inputs. The comparison of M5Rules model scenarios also showed that the eighth scenario with RMSE=1.85 (mm day-1), had higher accuracy than other scenarios. The seventh scenario of the M5P model has performed better than other scenarios due to its RMSE=1.86 (mm day-1). Finally, the evaluation of SVR model scenarios showed that the eighth scenario with RMSE=1.88 (mm day-1) had a better performance than other scenarios. The comparison of the models used to predict daily reference evapotranspiration in Astara station showed that the fifth scenario of M5P and M5Rules models having evaluation criteria of R2=0.76, RMSE=1.42 (mm day-1), NS=0.7 and WI=0.89 had the highest accuracy compared to other models and showed the best performance. Also, the evaluation of the results of the models in Isfahan station showed that the eighth scenario of the M5Rules model, having the evaluation criteria of R2=0.8, RMSE=1.85 (mm day-1), NS=0.8 and WI=0.94 had the best performance compared to other models and the M5Rules model was selected as the best model. Also, the seventh scenario of the M5P model had almost the same performance as the eighth scenario of the M5Rules model and showed a good performance. Therefore, M5P and M5Rules models successfully predicted reference evapotranspiration. One of the limitations of the present study is the lack of access to dew point temperature and solar radiation data. Therefore, the use of these parameters is suggested for further studies.

Climate change and its consequence impacts on the different phenomena of earth are serious mankind concerns during recent years. Climate change and global warming have very significant negative impacts on different resources including water and ice resources, forests, pastures, agricultural fields, industry and finally human life. Air temperature and precipitation variations are primary effects of climate change on the atmospheric elements. Hence, the assessment of the atmospheric element for an instance temperature has critical importance. Temperature rise caused by climate change have serious negative impacts on agricultural activities through increasing the evaporation and the possibility of droughts. Because climatological elements have nonlinear behavior and they are not function of a certain statistic distribution therefore a tendency for using non-parametric approaches especially Mann-Kendall is growing. The complicated nature of physical processes and lack of adequate knowledge in the climate models have caused creating statistical models and their development for defining these processes. The application of these models for reconstruction of past values and predicting of future values has been called time series. The aim of current research is analyzing the variation trend of mean maximum monthly temperature using Mann-Kendall test, mean maximum monthly temperature with time series method, determining proper pattern and prediction of temperature variations at the Northwest of Iran in the following years.

In this research the trend of mean maximum temperature variations in 12 selected stations in Northwest of Iran in a 24 years period was investigated. At first, the trend of variation data series for was tested using Mann-Kendall approach. Then, mean maximum monthly temperature was predicted using time series model. Minitab 17 software was applied in order time series model development and prediction purposes. Total number of data for each set was 285 where 80% of them were considered for calibration and 20% for model validation. The performance of models was investigated based on Model Efficiency Coefficient (CE) and Correlation Coefficient (R) indices. The CE varies between ∞- to 1 and the closer values to 1 indicates more accurate model performance. Finally, temperature predictions were done for following 8 years based on developed models.

The obtained results of application of Mann-Kendall test for determining mean maximum temperature trend in 12 studied stations in the Northwest of Iran clarified an increasing behavior for all stations. Increasing trends in Ahar and Sarab station were significant at the level of 95% and in the Tabriz, Marageh, Miyaneh, Ardabil, Khalkhal, Urmia, Khoy and Mahabad stations the significance level was 99%. Regarding to the basic assumptions in time series modeling, before starting model creating, the normal and static situation of data series was tested. The obtained results of these tests also showed a linear increasing trend in the investigated stations. Consequently, seasonal and non-seasonal differential process on initial series in the studied stations was conducted to model recognize through ACF and PACF differential series graphs. The temperature variations along different seasons of year in all stations proved more increasing for all stations in the winter in comparison with other seasons.
Considering 12th differential level due to seasonal characteristic of data, ACF and PACF graphs of differential series were plotted and a correlation was observed between data in first lag. To create series model, seasonal model of SARIMA(p,d,q)(P,D,Q)ω was applied. After calibration and validation of final models for studied stations, these models were applied to for predicting 8 following years (2018-2026) and were compared with basic period (1994-2017). According to the predictions, mean maximum temperature in all station shows an increasing in comparison to the basic period. The highest increasing amount is for Jolfa station with 4.39˚C and the lowest value was determined for Parsabad station with 0.69 ˚C. The variations of temperature was assessed in seasonal scale for 8 upcoming years. The comparisons of temperature variation for all stations in the different seasons showed increasing behaviors in all stations in winter in comparisons with other seasons.

Mean maximum temperature in 12 studied stations was modeled by time series. High values for R and CE in these stations proved high accuracy of this method for predicting of air temperature. After model development and selection of the most proper model for studied stations, the prediction of temperature was performed for 8 following years for each station. The temperature variations in this duration were investigated seasonally and the results showed that the maximum temperature increasing for all stations will occur in the winter. Temperature increasing in winter months may cause negative impacts like change in precipitation pattern from snow to rain, early melting of region snow reservoirs, incomplete vernalization of the seeds and early start of growing season with a risk of frost hazard for crops.

In the hydrologic cycle, evaporation is the primary step that causes water loss. Evaporation takes into account various parts of the water balance under completely different climates, and its correct prediction is very important for water resources management. The importance of evaporation and its impact on surface water balance is highlighted through its relation to climate change and global warming. The latest outputs of meteorological models suggest that global warming has caused an increase in evaporation from the land surface and surface water bodies, which is anticipated to have a serious impact over time on water resources management and the global population. In arid and semi-arid regions, accurate prediction of evaporation is very important for decision-makers due to water scarcity. Estimating daily evaporation with the highest accuracy and in the fastest possible time is essential to determine the water needs of different products, design irrigation programs, and manage water resources in different areas, especially when there is insufficient meteorological information. Evaporation has complex and non-linear behavior. Also, the evaporation parameter is not measured in some meteorological stations. Furthermore, meteorological stations are not correctly distributed in many developing countries including Iran. Since coastal areas have more evaporation than others, in many cases the amount of evaporation is higher than the global average. Despite the high importance of evaporation in coastal areas, very few studies have predicted this parameter in Iran. Moreover, accurate prediction of water loss in these areas leads to a better understanding of the hydrological cycle and is essential for optimal water management and agriculture. Thus, the purpose of this research is to predict daily evaporation values in four coastal stations of Abadan, Ramsar, Bandar Abbas, and Bandar Anzali.

The main meteorological parameters including average relative humidity, minimum relative humidity, maximum relative humidity average temperature, minimum temperature, maximum temperature, sunshine hours, and wind speed, under separate scenarios, as input for support of vector regression (SVR) and SVR with firefly algorithm (SVR-FFA) for estimating evaporation values were used on a daily scale. Statistical parameters in the time period of 1990-2021 were utilized as input to the mentioned models. In order to evaluate the performance of the implemented models, various statistical parameters were used, including correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe coefficient (NS), and Willmott's Index of Agreement (WI). To better estimate the daily evaporation values, eight different scenarios were used as the combinations of input parameters.

Based on the obtained results for all studied stations, the SVR-FFA-8 showed the least error with RMSE = 2.843 (mm day-1) for Abadan station, RMSE = 1.13 (mm day-1) for Ramsar station, RMSE = 1.985 (mm day-1) for Bandar Abbas station and RMSE = 1.225 (mm day-1) for Bandar Anzali station. For the indices of correlation coefficient, Nash-Sutcliffe coefficient, and Wilmott’s index of agreement, the SVR-FFA-8 model also indicated in the highest values between observed and predicted amounts. Also, the indices of correlation coefficient, Nash-Sutcliffe coefficient, and Wilmott’s index of agreement illustrated the highest accuracy in Abadan station for all combinations compared to other stations, which shows the high correlation of observed and predicted values in this station. After SVR-FFA-8, SVR-FFA-7 model in Abadan and Bandar Anzali stations and the SVR-FFA-6 in Ramsar and Bandar Abbas stations showed acceptable performance. Thus, the RMSE for Abadan and Bandar Anzali stations is 2.995 (mm day-1) and 1.272 (mm day-1), respectively, and for Ramsar and Bandar Abbas, 1.176 (mm day-1) and was obtained 1.993 (mm day-1). Comparing the results of SVR combinations also revealed that for Abadan, Ramsar, and Bandar Anzali stations, SVR-8 and for Bandar Abbas station, SVR-6 showed the highest accuracy among all SVR combinations in all four studied stations. Also, Ramsar station presented the lowest RMSE compared to other stations. After the SVR-8 model for Abadan, Ramsar, and Bandar Anzali stations, the SVR-7 and SVR-6 models for the Bandar Abbas station showed a weaker performance due to having less input parameters. The comparison between the input parameters also concluded that the sunny hours is the most important parameter in predicting the daily evaporation values in all four stations, thus increasing the accuracy of the models.

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%.

In order to plan and make correct policies in agricultural sector in terms of sustainable application of water resources and ensuring food security and self-sufficiency in the production of strategic products such as wheat, it is necessary to make an accurate prediction of dryland wheat yield. Recently, use of artificial intelligence methods for this purpose has increasingly attracted researchers' attention. In this study, the performance of three ANN algorithms, BR, GD, and LM were evaluated and compared to estimate dryland wheat yield. Meteorological data (2004-2018) from 10 meteorological stations, located in Kermanshah province, were used as input data in the proposed models. After determining the correlations between meteorological parameters and dryland wheat yield, relative humidity (RH) and precipitation (P) variables under three different input data combinations were used in the proposed models. Comparison of the predicted and observed data of dryland wheat yield showed acceptable performance of all three models. The R2 values of training step for the best combination of input data for the ANN algorithms (BR, GD and LM) were 0.85, 0.64 and 0.74, respectively, and the RMSE values were 0.09, 0.16 and 0.18 tons per hectare, respectively. Comparison of the results of different input data combinations showed that the P parameter has the most importance in predicting the yield of dryland Wheat, however, the use of P and RH data simultaneously as the third scenario leads to the highest accuracy. Finally, the BR algorithm by combining the inputs of P and RH with R2 and RMSE values for the test data equal to 0.85 and 0.09 ton/ha respectively, as the optimal model in estimating the drtyland Wheat Yield compared to other algorithms and input combinations were known.

The Severe shortage of surface and groundwater resources is the most important constraint on sustainable agricultural development in arid and semi-arid regions. Frequent droughts in recent decades have also led to a significant drop in groundwater levels due to uncontrolled abstraction. In the present study, different irrigation scenarios are provided to reduce the withdrawal of water from the aquifer of Kavar plain in Fars province. The results of various studies show that due to climate change, water resources are reduced and irrigation requirements of plants will be increased during the growing season (Goodarzi et al., 2015; Zeinoddini et al., 2019). In this study, to simulate future climate parameters, the outputs of AOGCMs models and emission scenarios RCP2.6, RCP4.5, and RCP8.5 of the Fifth Climate Change Report were used (IPCC,2014). Then irrigation requirements of the study area were calculated by Cropwat software considering the cultivation pattern for the future period. Finally, by defining irrigation scenarios, the simultaneous effects of climate change and cultivation pattern change in the development of irrigation systems (surface and pressurized) on groundwater resources were evaluated.

The use of accurate and continuous data series is a necessary condition for most statistical and hydrological studies. Due to the importance of precipitation as one of the most important climatic and hydrological variables, in the present study, in order to predict the daily precipitation of Tabriz, Copula functions were used and the results were compared with intelligent methods and classical statistics. To predict precipitation in Tabriz station, precipitation data of Sarab, Sahand, and Maragheh stations were also used as auxiliary stations. Based on the obtained results, among all the methods studied, the M5 method with RMSE values of 3.14 mm, and the MAD method with 2.13 mm and the RF method with RMSE values of 5.18 mm and MAD 3.04 mm have the highest and lowest accuracy in estimating precipitation events, respectively. Among Archimedean copulas, the RMSE and MAD values for the Gambel function are 3.89 and 2.51 mm, respectively. Despite the range of estimation data is still very close to other methods, considering the capabilities of Copula functions, including the ability to apply multiple conditions and its probabilistic nature, which considers the behavior of the phenomenon, it can be acknowledged that in similar circumstances, the ability of Copula functions to estimate the missing data of phenomena such as rainfall is acceptable.

Wheat as the main food in the country is of particular importance. Wheat is not only an important economic agricultural commodity in the world, but also known as a powerful lever in political and global relations. Therefore, the analysis and forecast of the production status of this product in the country has always been the focus of attention. The purpose of this study is to predict the amount of wheat yield (X) using artificial intelligence in the annual time scale in Alborz province. For this purpose, using annual cultivation and production data, wheat yield was investigated in six cities of Nazarabad, Savojbalagh, Karaj, Eshtehard, Fardis and Taleghan with a period of 40 years (1981-2020). After calculating the yield (ton per hectare) and forming an annual time series, four artificial intelligence methods including the best neighbor algorithm (KNN), backup vector (SVM), gene expression planning (GEP) and Bayesin Network (BN) were used and the wheat yield was predicted for the following year. Results indicated a more precision in yield prediction in the years with more production; According to the results of the BN, GEP, SVM and KNN model, the correlation coefficient between the observed and anticipated wheat yield values was 0.84, 0.89, 0.89 and 0.92, respectively. Explaining that Karaj and Taleghan cities have the highest and lowest wheat production respectively. The results showed that the KNN method had the best accuracy among the others, as the values of R, RMSE and MAE varied from 0.84 to 0.92, 0.21 to 0/24 and 0.11 to 0.18. Overall, by comparing the proposed methods, the KNN method had the highest and the BN method had the least accuracy to predict the amount of wheat yield in Alborz province. The results of this study can be very useful in providing and managing food security in areas under study.

The impact of dust phenomenon in Iran is so vast that it has involved more than half of the country's provinces in some way with the issues and limitations of this natural phenomenon. In addition to the environmental effects, it has disrupted the implementation of national sustainable development plans and so far, it has had many negative consequences. This research tries to present a new hybrid model using artificial intelligence hybrid metamodels as well as Box Jenkins hybrid metamodels to predict and model the FDSD index (frequency of days with dust storms), in seven synoptic stations of Khuzestan province with length The statistical period has been 40 years (1981-2020). The hybrid prediction algorithms used in this research include W-ANFIS, AF-SVM, ARIMA-NARX and SARIMA-SETAR. The prediction results showed that the decrease in the performance of hybrid models to predict the FDSD index has a direct relationship with the decrease in the frequency of days with dust storms. So that the correlation coefficient for experimental data in AF-SVM and W-ANFIS hypermodels from 0.991 and 0.985 to 0.985 and 0.958, respectively, and Nash Sutcliffe coefficient has also decreased from 0.977 and 0.960 to 0.973 and 0.952, respectively. Also, the RMSE coefficient from Abadan station to Dezful for the two metamodels from 0.135 and 0.151 to 0.140 and 0.179 respectively, And the MAE coefficient has also increased from 0.054 and 0.068 to 0.060 and 0.093, respectively. Correlation coefficient for test data in Box Jenkins SARIMA-SETAR and ARIMA-NARX hypermodels also from 0.967 and 0.951 to 0.958 and 0.941 respectively and the Nash Sutcliffe coefficient has also decreased from 0.945 and 0.923 to 0.938 and 0.913, respectively, which indicates the weakening of the performance of hybrid metamodels with the decrease in the frequency of dust storms in Khuzestan province. Also, by fitting four hybrid hypermodels on the FDSD index, it was shown that AF-SVM hybrid hypermodel had better performance than other methods. In a way, in all studied stations, the correlation coefficient and Nash-Sutcliffe coefficient are higher and the root mean square error coefficient and the mean absolute value of the error are lower, which shows the superiority of this hybrid meta-model over other meta-models for predicting the FDSD index in Khuzestan province. The results of this study can be used to model dust storms in other western regions of the country.

There is little information about the spatial distribution of elements involved in assessing the fertility of soils in Khuzestan province, especially the available phosphorus contents of the soils. Therefore, this study conducted to determine the most effective soil properties controlling the concentration of available phosphorus contents of soils in the north of Khuzestan province and to introduce the most appropriate method for modeling the spatial distribution of available phosphorus contents of the soils analyzed using linear regression and random forest algorithm. For this purpose, 250 composite soil samples (0-10 cm depth) were randomly collected using the Conditional Latin Hypercube sampling approach from December 2020 to February 2021. Then, the physical and chemical properties of the samples were determined using standard laboratory methods. The experimental data were then analyzed for descriptive statistics using SPSS software. To model the spatial variability of available phosphorus contents of the soils, the experimental data were modeled using linear regression and random forest models in RStudio software. The results showed that according to the measured amounts of absorbable phosphorus in the soil samples in 32.4% of the samples, the concentration of available phosphorus is less than 5 mg/kg. Evaluation of multiple linear regression and random forest models based on model evaluation metrics including mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) using training, test and the whole dataset, showed that the random forest model provides better and more accurate estimates due to higher coefficients of determination as well as lower error values. The results also illustrated that the organic carbon content of the soils has the greatest contribution in the study area to predict available contents of soil phosphorus. In conclusion, models that include non-linear relationships between variables seem to be more suitable in predicting soil properties.

Dust has always been one of the most important environmental hazards and has adverse environmental consequences. The purpose of this study is to investigate the relationship between temperature extreme variables and dust storms and evaluate the best model for predicting the FDSD index in the west of the country. We used hourly visibility data, World Meteorological Organization codes and temperature extreme indices including maximum temperature (TXx) and minimum temperature (TNn) on a monthly basis for 14 meteorological stations located in the west of the country with a statistical period of 25 years (1990-2014) and correlation between them were considered using Tau-Kendall and Pearson correlation coefficients. Map of correlation coefficients to better display the results was prepared by spline method (base radius method) in ArcGIS software. Also, three artificial intelligence models including best neighbor algorithm (KNN), gene expression programming (GEP) and Bayesian network (BN) were evaluated to predict dust. The results showed that dust storms have a positive and significant correlation with temperature extreme indices in 14 studied stations, so that the highest Tau-Kendall correlation coefficient with FDSD index is related to the maximum temperature variable in Dogonbadan station with a value of 0.202 and with the minimum temperature at Sare-Pole-Zahab station with the correlation coefficient 0.242. Also, the highest Pearson correlation coefficient with FDSD index for the maximum temperature variable in Dogonbadan station was 0.415 and that of the minimum temperature in Islamabad station 0.211. Also, the results of the forecast indicated the proper performance of the KNN method, which is ranked first in 13 stations and the BN method had the best performance in Islamabad station. The results of this study can help to better understand the occurrence of dust storms and to studying their climatic relations, as well as to reducing the damage caused by this phenomenon in the study area.

For sustainable land use, it is necessary to reveal the changes in land use and land cover and to identify the factors affecting the changes. The use of remote sensing and GIS provides accurate and systematic information on surface phenomena. The purpose of this study is to evaluate the efficiency of the integrated model of automatic cell and Markov chain in simulating and predicting temporal and spatial changes of land use changes and land cover in Gotvand dam area. The Kappa coefficient was 0.92 for 1991, 0.97 for 2008, and 0.93 for 2020. The accuracy of the CA-MARKOV model for predicting changes in the second period was 85%. Pastures form the dominant cover of the study area. Also, pasture and barren lands have been destroyed and reduced in area, and the area of other uses has increased. The results of the change prediction matrix based on the map of 2008 and 2020 showed that between 2020 and 2050, 10.37% of water areas, 21.49% of built-up areas, 44.41% of agricultural lands, 25.85% of Barren lands, 41.10% of pastures and 18.15% of meadows will remain unchanged. The results of revealing the land use map of 2050 showed that 31.2% of water areas, 37.5% of built-up areas, 13.9% of agricultural lands, 32.37% of barren lands and 44.78% of pastures in the studied area. The results show the increase of cultivated area and the development of agricultural lands. Also, the results showed that the increase in population and expansion of urbanization in the region has caused the destruction of pastures.

The study of spatial distribution of soil properties for optimal soil management and proper utilization of non-renewable soil resources is of particular importance. The copula function is one of the new interpolation techniques that are widely used in various sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variation of some soil properties using the copula function and to compare with geostatistics techniques. Sampling by regular networking was done in a 484 ha area in the west of Baft city, Kerman province, and 121 surface soil samples were collected. After air drying and passing through a 2 mm sieve, the percentage of organic matter and clay were determined in soil samples. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple kriging, ordinary kriging, universal kriging and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), mean absolute error (MAE), and Mean Bias error (MBE). In order to fit the copula function on the data, the distribution function of the studied variables was determined. The results showed that the distribution of each of the studied variables is different and is explained by different distribution functions. Also, with increasing distance, the value of correlation for all studied variables decreased so that after a distance of 2000 meters, they do not show any spatial correlation. Comparison of the Copula function and geostatistical techniques based on evaluation criteria showed that the Copula function had a better performance in estimating the studied variables and the estimation error for the Copula function were calculated less. In general, the results of this study showed that due to the skewed nature of soil data, Copula function have the ability to fully express the probabilistic dependence and can be considered in spatial studies.