Efficiency evaluation of low impact development practices on urban flood risk.

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2-22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

Habitat suitability modeling of Descurainia sophia medicinal plant using three bivariate models.

Climate change has caused medicinal plants to become increasingly endangered. Descurainia sophia (flixweed) is at risk of extinction in Fars Province, Iran, due to climate change and modifications of land use. Flixweed is highly valuable because of its medicinal properties. The conservation of this species using habitat suitability modeling seems necessary. In this research, the geographical locations of D. sophia's distribution in southern Iran were recorded and mapped using ArcGIS 10.2.2. Then, ten important variables affecting the growth of D. sophia medicinal plants were identified and prepared as thematic layers. These variables were, namely, "elevation," "slope degree," "slope aspect," "soil physical characteristics (sand, silt, and clay percentage)," "soil chemical properties (EC and pH)," "annual mean rainfall," "annual mean temperature," "distance to roads," "distance to rivers," and "plan curvature." In this study, three bivariate models, including the "index-of-entropy (IofE)," "frequency ratio (FR)," and "weight of evidence (WofE)," were used for mapping the habitat suitability of D. sophia. Moreover, the ROC curve and AUC index were used for evaluating the accuracy of the models. Based on the results, the IofE model ("AUC": 0.93) was the most accurate, while the FR ("AUC": 0.92) and WofE ("AUC": 0.90) models ranked second and third, respectively. The models in this study can be applied as tools for the protection of endangered medicinal plants. Furthermore, the map could assist planners, decision-makers, and engineers in extending study areas. By determining the habitat maps of medicinal plants, their extinction can be prevented. Such maps can also assist in the propagation of medicinal plants.

Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran.

As one of the largest rivers in the southwest of Iran, the Kor River plays an important role in local economy and ecosystem. However, the rapid development of industry has caused significant pollution in this river in recent years. Despite of a number of studies reported on this river regarding water pollution, few have conducted a comprehensive investigation of a wide range of water quality parameters to map the current pollution status. This study focuses on 21 water quality parameters around the industrial centers of the Kor River basin with samples taken from 25 stations. With the measured parameters, the interpolation maps of each parameter were determined using the Kriging method, and the water quality was quantified using the Water Quality Index (WQI) method. The results showed that the WQI values were between 28 and 73, showing more pollution around the factories than in the upstream areas. The results of the principal component analysis (PCA) indicated that BOD, COD, NO3-, and coliforms were the most important parameters among the 21 parameters affecting the water quality. Linear regression results suggested that the best parameters for determining coliforms and WQI values were BOD, and Cr, PO43-, Hg and Zn levels, respectively, with R2 greater than 0.87. These results can also simplify the prediction of coliforms and WQI using only a few parameters. We further found that flatter regions generally had more pollution, primarily due to pollutant accumulation as a result of water stagnation.

Investigating geometrical characteristics of collapsed pipes and the changing role of driving factors.

Determination of the amount (i.e., area and volume) of soil losses due to erosional landforms, especially collapsed pipes, plays a considerable role in different decision-making approaches. Further, mapping the spatial distribution and predicting the volumetric and areal losses of collapsed pipes (CPs) are essential for supporting ecosystem health. The study was conducted in relation to the area and volume of CPs and their related covariables. It focused on the estimation of soil losses due to collapsed pipes using unmanned aerial vehicle (UAV) images as well as field covariates at the Chatal Watershed, Golestan Province, Iran. A total of 481 soil samples were collected from homogeneous units with an area of approximately 1,410 ha. The potential relationship between the area/volume of collapsed pipes and land use, several topographic attributes (i.e., altitude, slope, and aspect), and soil properties, including soil stability, soil organic matter, clay, silt, and sand contents were analyzed using five distance-based methods (i.e., kernel density (KD), average nearest neighbor (ANN), spatial autocorrelation, hotspot analysis (HSA), and ordinary least square (OLS) analysis. The average nearest neighbor (Ratio = 0.12, Z score = -20.30, p-value < 0.05) and Moran space solidarity (Moran index = 0.258, Z score = 5.50, p-value < 0.05) showed the cluster distribution of area and volume of CPs. Hot spots and cold spots in the southwestern part of the study area were identified using KD and HSA. The relationship between existing independent and dependent variables (area of CPs) using regression analysis of OLS showed that slope and aggregate stability (>2.5 standard deviation) had the highest positive relationship with the dependent variable. Regarding the volume of CPs, land use (especially agricultural lands) had the strongest relationship with the dependent variable. Thus, geometrical characteristics of collapsed pipes can be applied as a quantitative indicator for the identification of hotspot zones (hazardous areas), land use planning, and erosion hazard mitigation. However, more studies are required to measure geometrical characteristics of soil landforms.

Ecological risk potential assessment of heavy metal contaminated soils in Ophiolitic formations.

The purpose of this study is to generate maps of contamination risk for cadmium (Cd), copper (Cu), lead (Pb), nickel (Ni), and zinc (Zn) in soils of a large alluvial fan located in Neyriz, Iran and to investigate their possible entry into the food chain. To this aim, the concentrations of the heavy metals of the soils are measured. The Geo-accumulation index (Igeo), Muller index, and potential ecological risk index are then used to evaluate soil contamination. The spatial distribution map of elements is also prepared using the kriging method. The results show that the Cd concentration in soils (mean 23 mg/kg) is 10-40 times higher than the global standard threshold (0.30-0.70 mg/kg), the Ni concentration (mean 13 mg/kg) is lower than the threshold (34 -12 mg/kg), the Cu concentration (mean 19.39 mg/kg) is below the threshold (24-13 mg/kg), the Zn concentration (mean 14.11 mg/kg) is also below the threshold (45-100 mg/kg), and the Pb concentration (mean 93.78 mg/kg) is higher than the threshold (44-22 mg/kg). The accumulation index values for Pb and Cd are 1.61 and 5.3, respectively, which decrease from the top to bottom of the study area. The enrichment factor values for Cu, Zn, Pb, Cd, and Ni are 0.43, 0.14, 4.60, 62.57, and 0.27, respectively, which also decrease from top to bottom. The accumulation index values in the soils confirm the occurrence of contamination and further indicate that the elements in the soils originated from local materials and Ophiolitic formations masses in the area. Overall, this research for the first time investigates the effect of natural factors (geological formation) on the soil and plant pollution in the study area and shows that, in addition to pollution by human activity, natural factors such as type of formation can lead to soil and plant pollution.

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh.

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

Determining the geographical model and environmental resilience patterns in Iranian provinces.

Considering environmental resources as a context for sustainable development is of paramount importance. Iran has faced a variety of environmental problems over the past few decades due to population development, changing patterns of residence, and urban development. Resilience measures the adaptation to changes occurring after unwanted events. Therefore, this study aimed to evaluate environmental resilience (natural and human factors) among the Iranian provinces. Then, the environmental resilience index was categorized through a data mining method, and the required measures for each pattern were presented according to the obtained patterns. Based on the results, Semnan Province had the highest environmental resilience, whereas Tehran, Alborz, Hormozgan, Isfahan, Khorasan Razavi, Khuzestan, and Sistan and Baluchestan had the lowest environmental resilience index (ERI). In addition, the results of data mining indicate that the provinces have five distinct patterns. CO2 emissions and drinking water access indicators have the highest and lowest importance in the formation of patterns, respectively. Furthermore, the environmental resilience of Tehran Province was influenced more by both indicators of energy use and CO2 emissions. Therefore, improvements in energy efficiency, developing public transportation, using standard fuels, and modernizing old industries should be considered as ways to increase productivity. The results of resilience patterns significantly help planners and managers develop policies and programs to increase environmental resilience.

Habitat potential modelling and mapping of Teucrium polium using machine learning techniques.

Determining suitable habitats is important for the successful management and conservation of plant and wildlife species. Teucrium polium L. is a wild plant species found in Iran. It is widely used to treat numerous health problems. The range of this plant is shrinking due to habitat destruction and overexploitation. Therefore, habitat suitability (HS) modeling is critical for conservation. HS modeling can also identify the key characteristics of habitats that support this species. This study models the habitats of T. polium using five data mining models: random forest (RF), flexible discriminant analysis (FDA), multivariate adaptive regression splines (MARS), support vector machine (SVM), and generalized linear model (GLM). A total of 119 T. poliumlocations were identified and mapped. According to the RF model, the most important factors describing T. polium habitat were elevation, soil texture, and mean annual rainfall. HS maps (HSMs) were prepared, and habitat suitability was classified as low, medium, high, or very high. The percentages of the study area assigned high or very high suitability ratings by each of the models were 44.62% for FDA, 43.75% for GLM, 43.12% for SVM, 38.91% for RF, 28.72% for MARS, and 39.16% for their ensemble. Although the six models were reasonably accurate, the ensemble model had the highest AUC value, demonstrating a strong predictive performance. The rank order of the other models in this regard is RF, MARS, SVM, FDA, and GLM. HSMs can provide useful output to support the sustainable management of rangelands, reclamation, and land protection.

Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling.

This study assesses forest-fire susceptibility (FFS) in Fars Province, Iran using three geographic information system (GIS)-based machine-learning algorithms: boosted regression tree (BRT), general linear model (GLM), and mixture discriminant analysis (MDA). Recently, BRT, GLM, and MDA have become important machine-learning algorithms and their use has been enriched by application to various fields of research. A database of historical FFs identified using Landsat-8 OLI and MODIS satellite images (at 358 locations) and ten influencing factors (elevation, slope, topographical wetness index, aspect, distance from urban areas, annual mean temperature, land use, distance from road, annual mean rainfall, and distance from river) were input into a GIS. The 358 sites were divided into two sets for training (70%) and validation (30%). BRT, GLM, and MDA models were used to analyze the spatial relationships between the factors influencing FFs and the locations of fires to generate an FFS map. The prediction success of each modelled FFS map was determined with the help of the ROC curve, accuracy, overall accuracy, True-skill statistic (TSS), F-measures, corrected classify instances (CCI), and K-fold cross-validation (4-fold). The accuracy results of training and validation dataset in the BRT (AUC = 88.90% and 88.2%) and MDA (AUC = 86.4% and 85.6%) models are more effective than the GLM (AUC = 86.6% and 82.5%) model. Also, the outcome of the 4-fold measure confirmed the results from the other accuracy measures. Therefore, the accuracies of the BRT and MDA models are satisfactory and are suitable for FFS mapping in Fars Province. Finally, the well-accepted neural network application of learning-vector quantization (LVQ) reveals that land use, annual mean rainfall, and slope angle were the most useful determinants of FFS. The resulting FFS maps can enhance the effectiveness of planning and management of forest resources and ecological balances in this province.

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study.

Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

Using machine learning algorithms to map the groundwater recharge potential zones.

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.

How do data-mining models consider arsenic contamination in sediments and variables importance?

Arsenic (As) is one of the most important dangerous elements as more than 100 million of people are exposed to risk, globally. The permissible threshold of As for drinking water is 10 µg/L according to both the WHO's drinking water guidelines and the Iranian national standard. However, several studies have indicated that As concentrations exceed this threshold value in several regions of Iran. This research evaluates an As-susceptible region, the Tajan River watershed, using the following data-mining models: multivariate adaptive regression splines (MARS), functional data analysis (FDA), support vector machine (SVM), generalized linear model (GLM), multivariate discriminant analysis (MDA), and gradient boosting machine (GBM). This study considers 12 factors for elevated As concentrations: land use, drainage density, profile curvature, plan curvature, slope length, slope degree, topographic wetness index, erosion, village density, distance from villages, precipitation, and lithology. The susceptibility mapping was conducted using training (70%) and validation (30%). The results of As contamination in sediment showed that classifications into 4 levels of concentration are very similar for two models of GLM and FDA. The GBM calculated the areas of highest arsenic contamination risk by MARS and SVM with percentages of 30.0% and 28.7%, respectively. FDA, GLM, MARS, and MDA models calculated the areas of lowest risk to be 3.3%, 23.0%, 72.0%, 25.2%, and 26.1%, respectively. The results of ROC curve reveal that the MARS, SVM, and MDA had the highest accuracies with area under the curve ROC values of 84.6%, 78.9%, and 79.5%, respectively. Land use, lithology, erosion, and elevation were the most important predictors of contamination potential with a value of 0.6, 0.59, 0.57, and 0.56, respectively. These are the most important factors. Finally, these data-mining methods can be used as appropriate, inexpensive, and feasible options to identify As-susceptible areas and can guide managers to reduce contamination in sediment of the environment and the food chain.

Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models.

Flooding is a very common worldwide natural hazard causing large-scale casualties every year; Iran is not immune to this thread as well. Comprehensive flood susceptibility mapping is very important to reduce losses of lives and properties. Thus, the aim of this study is to map susceptibility to flooding by different bivariate statistical methods including Shannon's entropy (SE), statistical index (SI), and weighting factor (Wf). In this regard, model performance evaluation is also carried out in Haraz Watershed, Mazandaran Province, Iran. In the first step, 211 flood locations were identified by the documentary sources and field inventories, of which 70% (151 positions) were used for flood susceptibility modeling and 30% (60 positions) for evaluation and verification of the model. In the second step, ten influential factors in flooding were chosen, namely slope angle, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, rainfall, geology, land use, and normalized difference vegetation index (NDVI). In the next step, flood susceptibility maps were prepared by these four methods in ArcGIS. As the last step, receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated for quantitative assessment of each model. The results showed that the best model to estimate the susceptibility to flooding in Haraz Watershed was SI model with the prediction and success rates of 99.71 and 98.72%, respectively, followed by Wf and SE models with the AUC values of 98.1 and 96.57% for the success rate, and 97.6 and 92.42% for the prediction rate, respectively. In the SI and Wf models, the highest and lowest important parameters were the distance from river and geology. Flood susceptibility maps are informative for managers and decision makers in Haraz Watershed in order to contemplate measures to reduce human and financial losses.

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (≈70 %) locations were used for the spring potential mapping, while the remaining 259 (≈30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran.

Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6%) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.

Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method.

The escalating growth of the global population has led to degraded water quality, particularly in seawater environments. Water quality monitoring is crucial to understanding the dynamic changes and implementing effective management strategies. In this study, water samples from the southwestern regions of Iran were spatially analyzed in a GIS environment using geostatistical methods. Subsequently, a water quality map was generated employing large and small fuzzy membership functions. Additionally, advanced prediction models using neural networks were employed to forecast future water pollution trends. Fuzzy method results indicated higher pollution levels in the northern regions of the study area compared to the southern parts. Furthermore, the water quality prediction models demonstrated that the LSTM model exhibited superior predictive performance (R2 = 0.93, RMSE = 0.007). The findings also underscore the impact of urbanization, power plant construction (2010 to 2020), and inadequate urban wastewater management on water pollution in the studied region.

Using machine learning to predict processes and morphometric features of watershed.

The research aims to classify alluvial fans' morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (Af) and minimum fan height (Hmin-f). The feature selection algorithm identified (Hmin-f), maximum fan height (Hmax-f), minimum fan slope, and fan length (Lf) to be the morphometries most important for determining formation material, and basin area, fan area, (Hmax-f) and compactness coefficient (Cirb) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87).

An applicability test of the conventional and neural network methods to map the overall water quality of the Caspian Sea.

This study investigates the water quality of the Caspian Sea by examining the presence of nutrients and heavy metals in the water. Water samples were collected from 22 stations and analyzed for nutrient and heavy metal levels. The study used the fuzzy method to prepare water quality maps and employed ANNs methods to predict microbial contamination for future years. The results revealed that the western and northwestern parts of the region had higher nutrient levels (about 40.2 % of the region), while the eastern and northeastern shores were highly polluted due to increased urbanization (about 70.1 % of the region). The long short-term memory (LSTM) method was found to have the highest accuracy compared to other ANNs methods and indicated a recent increase in pollution (RWater quality2=0.940, ROECD2=0.950, RTRIX2=0.840). The study recommends targeted research to identify the causes and means of controlling pollution in light of the predicted increase in pollution in the Caspian Sea.

Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models.

Floods are among the most destructive disasters because they cause immense damage to human life, property (land and buildings), and resources. They also slow down a country's economy. Due to the dynamic and complex nature of floods, it is difficult to predict the areas that are prone to flooding. In this study, an attempt was made to create a suitability map for future urban development based on flood vulnerability maps for the catchment area of Taif, Saudi Arabia. Three models were used for this purpose, including bivariate (FR), multivariate (LR), and machine learning (SVM) were used. Thirteen parameters were used as flood-contributing parameters. The inventory map was constructed using field surveys, historical data, analysis of RADAR (Sentinel-1A), and Google Earth imagery collected between 2013 and 2020. In general, 70% flood locations were randomly selected from the flood inventory map to generate the flood susceptibility model, and the remaining 30% of the flood locations were used for model validation. The flood susceptibility map was classified into five zones: very low, low, moderate, high, and very high. The AUC value used to predict the performance of the models showed that the accuracy reached 89.5, 92.0, and 96.2% for the models FR, LR, and SVM, respectively. Accordingly, the flood susceptibility map produced by the SVM model is accurate and was used to produce a flood vulnerability map with the help of urban and road density maps. Then slope and elevation maps were integrated with the flood vulnerability model to produce the final suitability map, which was classified into three zones: isolated zone, low suitability, and high suitability areas. The results showed that the highly suitable areas are located in the east and northeast of the Taif Basin, where the flood risk is low and very low. The results of this work will improve the land use planning of engineers and authorities and take possible measures to reduce the flood hazards in the area.

Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data.

Riparian forests in Iran are valuable ecosystems which have many ecological values. Because of destruction of these forests in recent years, providing spatio-temporal information from area and distribution of these ecosystems has been receiving much attention. This study was performed for mapping distribution, area and density of riparian forests in southern Iran using Sentinel-2A, Google Earth, and field data. First Sentinel-2A satellite image of the study area was provided. The field work was performed to take the training areas and to assess the forest density of riparian forests in Khuzestan province. In the first part of this study, after selecting training areas as pixel-based samples on the Sentinel-2A satellite image, supervised classification of image was performed using support vector machine (SVM) algorithm to classify the distribution of riparian forests. After classification of Sentinel-2A satellite image, the boundary of riparian forests map was checked and corrected on Google Earth images. In the second part of this study, field data, Normalized Difference Vegetation Index (NDVI), and regression model were used to assess the density of riparian forests. Finally, the accuracy of the final riparian forest map (showing both distribution and density of riparian forests) was assessed using Google Earth images. Results showed that the final riparian forest map (showing both distribution and density of riparian forests) with overall accuracy 89% and kappa index 0.81 had a good accuracy for classifying the distribution and density of riparian forests in Khuzestan province. These results demonstrate the accuracy of SVM algorithm for classifying the distribution of riparian forests and also capability of NDVI for classifying the density of riparian forests in this study. Results also showed that regression model (R2 = 0.97) is reliable for estimating riparian forest density. The results demonstrated that there are 68447.18 ha of riparian forest around the main rivers in Khuzestan province, mainly distributed in the northwest and southeast of the province. From this area, 54694.15 ha have been covered by dense forests and 13753.03 ha by sparse forests. Results of this research have created the useful data of area, distribution and density of riparian forests in 10-m spatial resolution which is necessary for conservation and management of these forests in southern Iran. It is suggested that mapping area, distribution and density of these forests would be performed using SVM algorithm and NDVI in the certain temporal periods for protective management of these ecosystems in time series.