seyed ahmad eslaminezhad

The purpose of this research is site selection of temporary accommodation centers in District 22 of Tehran with the help of effective criteria. These criteria are including Red Crescent, distance to bridge, distance to schools, distance to the main road, distance to the police station, distance to the gas source, distance to mosques, distance to food distribution centers, distance to metro lines, distance to Metro station, distance to health centers, distance to park and green space, distance to hospital, distance to fire stations, population density, earthquake risk, and slope. The novelty of this study is the use of an appropriate and sufficient combination of criteria for locating temporary accommodation sites. In this regard, the geographically weighted regression (GWR) method with two Gaussian and tri-cube kernels was used. This method is compatible with two unique properties of spatial data, namely spatial autocorrelation, and spatial non-stationarity. The values of R2 and RMSE obtained by the GWR method with tri-cube kernel were 0.9413 and 0.3470, respectively, which indicates the high compatibility of the tri-cube kernel with respect to the Gaussian kernel. The results also show that Chitgar Park and Azadi Sports Complex are some of the largest and most suitable areas for the construction of temporary shelters after the earthquake crisis in the case study.

Predicting groundwater potential is crucial for systematic development and planning of water resources. The main objective of this study is to develop machine learning models including Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) for predicting potential groundwater areas in the Birjand plain. Therefore, for the implementation of this study, geohydrological data related to 37 groundwater wells (including the number and location of wells and groundwater levels) and 17 hydrological, topographical, geological, and environmental criteria were used. Feature selection was performed using Support Vector Machine's least squares method to determine effective criteria for improving the performance of machine learning algorithms. Ultimately, predictive maps of groundwater potential were prepared using DT, RF, and SVM models, and the performance of these models was evaluated using the Area under the Curve (AUC) and other statistical indicators. The results showed that the DT model (AUC=0.89) has very high predictive capability for groundwater potential in the study area, and elevation was identified as the most important factor in predicting groundwater potential in this area. The findings of this study can serve as a guide for decision-making and appropriate planning in the optimal use of groundwater resources.

Different natural hazards which are sometimes caused by climatic processes such as fires, have put human life at stake throughout the course of history. Forest fire which is said to be one of the major causes of deforestation has been recognized as a major crisis in recent years. Satellite imagery and remote sensing techniques can effectively contribute to investigation of forest and rangeland fires. In the present study, the GEE system was used to assess the severity of fire in the Arasbaran Protected Area. For this purpose, Landsat 8 and Sentinel 2 satellite images as well as NBR (normal burning ratio) and dNBR indices were used for this purpose. According to the results, the map of areas with high fire risk can effectively contribute to forest fire management across the region.

Drought is a climatic phenomenon that almost happens in every climate situation, because Iran country is located in the arid belt of earth, the importance of drought and analyzing it in the past and future is important for us to programming and managing the water resources. In this research which was done in the study area of the watershed region of Kashfrud, the effect of climatic changes in the climatic precipitation parameter under two scenarios of RCP4.5 and RCP8.5 in the study period (19987-2016) was analyzed and surveyed. To analyze drought in the study region by using Standard Precipitation Index (SPI) and surface water supply index (SWSI), the occurrence and time of climatic and hydrological drought were analyzed. For predicting the future period precipitation by atmospheric general circulation model of MIROC5 and by using small scale (Delta) method under release scenarios RCP4.5 and RCP8.5, the future precipitation data were achieved, then standard precipitation index in the next 30 years’ period (2019-2048) was calculated. Using the implemented calculations in standard precipitation index and analyzing the results in the next 30 years, the number of dry and very dry months; means index number less than - 1.5, in the study area was achieved. Based on the achieved results from the standard precipitation index (SPI) in the annual scale in Mashhad and Golmakan station, the first decades of the study period (1987-1996) and future periods (2019-2028) are the driest periods. Also, the results of hydrologic drought index in annual scale in Mashhad station indicates 19 dry years and in Golmakan station indicates 16 dry years.

The purpose of this research is to select temporary accommodation centers after the earthquake crisis to meet the needs of the victims. Therefore, this research has tried to identify temporary accommodation sites in District 22 of Tehran with the help of effective criteria. The required data has been obtained from the Tehran Disaster Mitigation and Management Organization and the results of the general population and housing census of 2017. The novelty of this research is to present a new combination approach to determine the effective criteria for locating temporary shelter sites. In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) with a binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels was obtained at 0.04616 and 0.0097, respectively, which indicates the high compatibility of the tri-cube kernel with effective criteria. According to the obtained maps, Chitgar Park and Azadi Sports Complex are some of the widest and most suitable areas for the construction of temporary shelter sites after the earthquake crisis in the case study. By identifying temporary shelters, relevant organizations can provide the appropriate infrastructure for these selected centers so that there is no need to spend time to provide these services in the event of an earthquake.

Considering the harms of air pollution on human health and the environment, it seems necessary to reduce and solve this problem based on accurate knowledge of pollutants and criteria affecting it and identifying polluted areas. Therefore, using mathematical models in the form of machine learning is an optimal and cost-efficient approach to air pollution modeling. This research is applied in terms of purpose and its method is descriptive-analytical. The novelty of this research is presenting a new combination approach to determine the effective criteria for predicting the amount of air pollution. Therefore, the purpose of this study was to evaluate and compare the capabilities of two machine learning models, namely Support Vector Machine (SVM) and Random Forest (RF) in combination with Genetic Algorithm (GA) to predict air pollution in Tehran. The data used in this research include particulate matter and gaseous pollutants in Tehran in 2020, which was obtained from Tehran Traffic Control Company. MATLAB and ArcMap software were used to analyze the data. The value of coefficient of determination (R2) obtained from the combined RF-GA method was 0.997, which indicates the high compatibility of this model with the data of this study. Moreover, the Root Mean Square Error (RMSE) value from the combined RF-GA method was 0.153, which indicates high accuracy of this model. Based on the data obtained from Tehran Traffic Control Company, the results of the RF method indicate the appropriateness of selecting the model to estimate the amount of air pollution in Tehran.

Land subsidence is a natural hazard that occurs gradually in many regions of Iran. Investigating the causes of subsidence is important in order to prevent its progression, and decisions should be based on the characteristics of each region. While the drop in groundwater level is an important factor affecting subsidence, there is no direct linear relationship between changes in groundwater level and subsidence due to the complexity of other influencing factors. Therefore, this research aims to investigate the relationship between subsidence and changes in groundwater levels in the Esfarayen region of plain during the period of 2015 to 2021. The study analyzed the time series of radar interferometry and the water level of piezometric wells in the area of Esfarayen. The results show that there is a correlation between the decrease in groundwater level and the increase in subsidence in the studied area. However, due to the complexity of the relationship between subsidence and its contributing factors, further investigation and more comprehensive models are necessary.

Groundwater is exploited uncontrollably due to population growth and industrialization in different parts of the world. The purpose of this study is to evaluate the groundwater potential by advanced machine learning algorithms using topographical, hydrological, environmental, and geological criteria. To do this, three advanced machine learning algorithms were used, including Boosted Regression Tree (BRT), Logistic Model Tree (LMT), and Random Forest (RF). Therefore, for implementation, geo-hydrological data of 37 groundwater wells in Birjand plain of South Khorasan province were collected and randomly selected in a ratio of 70 to 30 were divided into training and validation data sets. Finally, groundwater potential maps were prepared using BRT, LMT, and RF algorithms. In order to validate the groundwater potential prediction algorithms, the area under the curve (AUC) and the statistical criteria of positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the LMT model (AUC = 0.865) has a better performance than the BRT and RF models in predicting the groundwater potential of the study area.

Predicting the potential of groundwater is very important for the systematic development and planning of water resources. The main purpose of this study was to develop ensemble machine learning models including random forest (RF), logistic regression (LR) and Naïve Bayes (NB) by random subspace Classifier (RS) algorithm to predict groundwater potential areas in Birjand plain. Therefore, for implementation, geo-hydrological data of 37 groundwater wells (Number of wells, location of wells and groundwater level or Water table) and 17 hydrology, topographic, geological and environmental criteria were used. The least squares support vector machine (LSSVM) feature selection method used to determine the effective criteria to increase the performance of machine learning algorithms. Finally, groundwater potential prediction maps were prepared using RF-RS, LR-RS and NB-RS models. The performance of these models evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that the RF-RS hybrid model (AUC = 0.867) has a very high predictability for groundwater potential in the study area. It was also found that the elevation criterion is most important in predicting the groundwater potential in the study area. The results of the present study can be useful for making appropriate decisions and planning regarding the optimal use of groundwater resources.

Due to the increase in the occurrence of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, it was tried zoning the areas of floods with the help of determining effective criteria. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present a new combination approach to determine the effective criteria in flood hazard zoning (Maneh and Samalqan County). In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) and binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels were obtained 0.0745 and 0.0022, respectively, which indicates higher compatibility of the tri-cube kernel than the Gaussian kernel. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.

Research on flood predicting models is one of the first steps in reducing flood damage and managing future floods in catchments. The aim of this study was to evaluate flood susceptibility in Birjand plain catchment through four machine learning models including support vector machine (SVM), J48 decision tree, random forest (RF) and Adaptive neuro fuzzy inference system (ANFIS). Therefore, in order to implement and validate the mentioned models, a list of flood-prone areas in the study area was prepared (42 flood-prone locations). In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood occurrence in the study area were extracted to be used to predict flood susceptibility map. The results showed that the highest accuracy was related to the RF model (0.845) and the lowest accuracy was related to the SVM model (0.791). In addition, the validation of the results using the ROC curve showed that the most accurate values of flood susceptibility belong to the RF model (AUC = 0.958). The results of this study can be used to manage vulnerable areas and reduce flood damage.

Today, road safety is a great concern for traffic engineers, because accidents have imposed extensive impacts on the quality of people’s life. Therefore, it is very important to predict accident-prone sections of the road network to prevent future accidents and improve traffic safety. One of the most important factors that cause accidents is the spatial structure of the road network. The spatial structure of the road network is related to the arrangement and layout of the road network components, which as a spatial constraint is very influential on urban flows. The novelty of this study is to present a new combination approach to determine the effective spatial structure parameters in predicting accident-prone sections (District 3 of Tehran city). In this regard, the combination of geographically weighted regression (exponential and bi-square kernels) and binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems, because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for exponential and bi-square kernels was obtained 0.064 and 0.003, respectively. It also found that spatial structure parameters had a significant impact on predicting accident-prone sections in the study area.

Groundwater is one of the most valuable resources for communities, agriculture, and industry. In the present study, three new artificial intelligence models, including Modified Real AdaBoost (MRAB), Bagging model (BA), and Rotation Forest model (RF), have been developed by the Functional Tree Base Classifier (FT) model to predict groundwater potential in Birjand plain area. Therefore, for implementation, geo-hydrological data of 37 groundwater wells and ten factors of topography, hydrology, and geology were used. The performance of these models was evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that although all the hybrid models developed in this study increased the prediction accuracy, MRAB-FT model (AUC = 0.742) has higher accuracy in predicting potential groundwater areas in Birjand plain. Accurate mapping of groundwater potential areas while maintaining a balance between consumption and operation will help feed the aquifer for optimal use of groundwater resources.

Due to rapid economic growth and over-exploitation of groundwater, nitrate contamination in groundwater has become very serious. The main purpose of this study is to develop a DRASTIC model to identify the vulnerability of groundwater to nitrate contamination. Therefore, the standard DRASTIC model was presented considering the land use factor (DRASTIC-LU model) to demonstrate the vulnerability of groundwater. The novelty of the present study is the development of DRASTIC and DRASTIC-LU models by support vector machine (SVM) to prevent the error of overlap and index methods. For implementation and validation of the models, 21 samples of observation wells were collected in Birjand plain aquifer. RMSE values for DRASTIC, DRASTIC-LU, DRASTIC+SVM, and DRASTIC-LU+SVM models were calculated to be 0.821, 0.743, 0.612, and 0.490, respectively, which was found that the hybrid models using SVM shows a better correlation between the amount of vulnerability and nitrate contamination. It was also found that the DRASTIC-LU+SVM model has a higher accuracy for assessing the vulnerability of groundwater to nitrate.

Floods are one of the most devastating types of natural disasters that every year causes the loss of human lives and properties around the world. The purpose of this study is to evaluate and compare the capability of three machine learning models namely Naïve Bayes Tree (NBTree), Alternating Decision Tree (ADTree), and Random Forest (RF) to predict flood risk in Maneh and Samalqan county. The novelty of the present study is the presentation of tree-based hybrid models that have been less used in previous research. To prepare a flood reference map in the study area, 300 flood-prone locations were identified and were divided into training and validation data sets through random selection with a ratio of 70 to 30. The spatial database of the flood was created using 15 hydrogeological and environmental criteria affecting the flood. Finally, three flood risk prediction maps were generated using NBTree, ADTree, and RF models. To validate the flood risk predicting models, the Area Under the Curve (AUC) factor and the statistical criteria of Positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the RF model had higher accuracy than the NBTree and ADTree models in predicting flood risk in the study area. The results also showed that the risk of flooding in the central areas of the study area is higher than other areas due to lower altitude and slope.

Seepage is one of the most substantial factors in the embankment dams design. The Cutoff wall method is the ideal construction solution for controlling flow through the foundation of earth dams. In this study, the cutoff wall method is investigated at the site of the Peygham-Chay dam (Study area). According to the dam site, in terms of its materials as well as their different permeability, water influx is a potential hazard at the site. For this purpose, SEEP/W program as the finite element approach was employed to estimate the seepage and design of the optimum depth of the sealing element. The results of numerical analyzes imply that the sealing component is not needed because of the low permeability in the primary 260 m at the left abutment of the dam. Also, the optimum depth of the cutoff wall is evaluated as 15 m at the rest of the dam axis.

Groundwater is the main source of drinking water in the semi-arid regions of Iran. Therefore, monitoring the quality of this valuable resource is vital. Hence, the purpose of this study was to investigate and prepare a groundwater quality map of Birjand located in the semi-arid region of eastern Iran using GIS-based geostatistical analysis with Drinking Groundwater Quality Index (DGWQI). For this purpose, the collected groundwater quality data of 27 agricultural wells during the years 2014-2019 were used. The results of spatial analysis show that 63% of the aquifer area was in the appropriate quality category, 18% in the poor category, 10% in the very poor category and 9% of the aquifer area was in the non-drinking category. Sensitivity analysis indicated that Mg2+, EC, and TDS parameters with the highest mean change index of 18.98, 20.68 and 19.04 were the most sensitive parameters in calculating DGWQI, respectively. Error evaluation calculated by R2 and RMSE methods on conventional kriging and spherical variogram models showed good performance for spatial analysis of all parameters including DGWQI, Mg2+, EC, and TDS. The DGWQI map shows that the western and southwestern parts of the aquifer do not have good quality conditions for extracting drinking water.

Increasing the occurrence of floods, especially in cities, and the risks to human, financial, and environmental risks due to its, make flood risk zoning of great importance. The purpose of this study is to estimate the flood risk of the Maneh and Samalghan based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present new combination approaches to determine the effective criteria in flood risk zoning (Maneh and Samalghan). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.1757, 0.0461, and 0.0097, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.