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Rivers are important water resources for human life, but sometimes cause irreparable damages. The flood plains are fertile terrains which are endangered by flood. Flood hazard mapping is one of the basic methods in flood fighting. In order to decline flood damages, the simulation of the hydraulic behavior of the rivers during flood occurrence is very important. In this study, areas that are flooded were zoned along Siahrood and Goharrood rivers (Rasht City, northern Iran). The rivers bed and banks terrains of Goharrood and Siahrood were simulated using HEC-GeoRAS extension and digital map (scale: 1000). Pick discharges with different return periods were estimated using stochastic analysis. HEC-RAS software and geographical information system were applied for simulating the hydraulic behavior of the rivers and providing flood zoning map. The GIS was an efficient tool for data-processing and mapping stages. Finally, the flood zones associated with 2, 10, 25, 50, 100 and 200 years return periods were mapped and necessary analysis were conducted during the present research. The results showed that some parts of Rasht City (river bank terrains) are endangered flood hazard.

Artificial neural networks (ANNs) have become one of the most promising tools for rainfall simulation since a few years ago. However, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in Kechik station, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP–ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L–M training algorithms were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L–M algorithm was more efficient than the CG and GDX algorithm, so it was used for training six ANN models for rainfall intensity forecasting. The results showed that all of the parameters were proper inputs for simulating rainfall, but temperature, evaporation and moisture were the most important factors in rainfall occurrence.

Urbanization is a pervasive global trend. The development of residential areas and road network in Hajighoshan watershed (northern Iran) has been observed in the recent several decades. The objective of this study is the quantitative investigation of the effects of impervious surfaces development and urban development on runoff generation and flood hazard. The study of urban area development was carried out using aerial photos, topographic maps and satellite images. Also a rainfall-runoff model was presented using GIS (HEC-GeoHMS extension) and HEC-HMS model. Then, the model was optimized with initial loss and SCS-Lag parameters. The optimized model was evaluated using the other 4 events of flood .In the next stage, the development of impervious surfaces was included in the validated hydrologic model and their effects on intensifying runoff generation and flood hazard was investigated quantitatively during the recent forty years. The results showed that the runoff generation potential has increased in Hajighoshan watershed because of urban development during forty years ago.

Springs are the important water resources, which thier study is necessary in terms of their management and exploitation. In northern Iran, the karst springs locate in the central Alborz highlands and alluvial springs on the Caspian southern coasts.The Karst and alluvial springs discharge is variable because of different conditions in terms of aquifer, topography and precipitation .This study has been done to simulate the karst and alluvial springs discharge and to compare their hydrologic conditions. So, 80 karst springs and 82 alluvial springs were investigated in central Alborz and on the Caspian southern coasts. Two models were presented to simulate the karst and alluvial springs discharge using Multivariate Regression analysis (SPSS software).Then, their efficiency was considered. Finally, the models and the results of regression analyses were compared. The results showed that the most important factors of the karst springs discharge are aquifer formation (its porosity) and site elevation, but about the alluvial springs, the hydraulic conductivity of aquifer formation and aquifer depth are the most important factors.

Soil erosion and sediment transport measurement is a time-consuming and difficult step yet important part of hydrological studies. Hence, use of models has become commonplace in estimating soil erosion and sediment transport. In this study, we used an artificial neural network (ANN) to simulate soil erosion rates. A geographic information system (GIS) was used as a pre-processor and post-processor tool to present the spatial variation of the soil erosion rate. The ANN was trained, optimized and verified using data from the Kasilian watershed located in the northern part of Iran. Field plots were used to estimate soil erosion values on the hillslopes. A Multi Layer Perceptron (MLP) network was adopted, where the soil erosion rate was the output variable and the rainfall intensity and amount, air and soil temperature, soil moisture, vegetation cover and slope were the inputs. After the training process, the network was tested. According to the test results, the ANN can estimate soil erosion with an acceptable level (coefficient of determination = 0.94, mean squared error = 0.04). The verified network and its inputs were used to estimate soil erosion rates on the hillslopes. Finally, a soil erosion rate map was generated based on the results of the verified network and GIS capabilities. The results confirm the high potential when coupling an ANN and a GIS in soil erosion estimation and mapping on the hillslopes. [ABSTRACT FROM AUTHOR]

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low. [ABSTRACT FROM AUTHOR]

Drinking water wells need to have clearly defined sanitary boundaries to ensure proper water quality. This study has been done to define these boundaries for drinking water wells in the Mazandaran plain using statistical and geographic information system (GIS) techniques. Here, an index known as the groundwater quality index (GWQI) was used to investigate the water quality. In the study area, 83 drinking water wells were selected, and for each drinking water well, the GWQI values were then estimated. Statistical analysis revealed that transmissivity of aquifer formations, groundwater depth, and distance from residential and industrial areas were significant factors on the groundwater quality. In the next step, we applied a multiple regression method using SPSS software in order to model the sanitary boundaries. Consequently, a linear model was presented to model the sanitary boundary as well as to provide the corresponding maps of these boundaries using the GIS techniques. The linear model, for each well, estimates the minimum distance from the contaminant centers that guarantees the normal GWQI values. Then, the validated model was applied in the GIS environment for zonation of the sanitary boundaries of the drinking water wells. Coupling statistical analysis with GIS technique capabilities could provide practitioners with easily interpretable water quality boundary maps in the management of these resources. [ABSTRACT FROM AUTHOR]

A new analytical methodology for computing synthetic seismograms in 3-D anelastic media is applied to the modelling of the local records of the 2003 Bam Mw 6.6 earthquake, Southeastern Iran. The method is based on the combination of the modal summation technique with the asymptotic ray theory. The 3-D models are determined by a set of vertically heterogeneous sections (1-D structures) that are juxtaposed on a regular grid. The distribution of these sections in the grid is done in such a way to satisfy the condition of applicability of the WKBJ approximation, that is, the lateral variation of all the elastic parameters has to be small with respect to the prevailing wavelength. The procedure, described very briefly, has been applied for the area of the destructive 2003 December 26 Bam earthquake Mw 6.6, with a maximum cut-off frequency of 6 Hz. Synthetic seismograms show fine agreement with recorded signals when comparing shapes, amplitudes and spectra and evidence the relevance of 3-D effects. [ABSTRACT FROM PUBLISHER]

While there are different methods and models that can be applied to estimate the qualitative and quantitative parameters of water resources, unfortunately, no comprehensive qualitative and quantitative data exist about water resources in Iran. The present study is to compare the performance of the artificial neural network (ANN) and the multivariate regression methods in simulating spring discharge in the Caspian Southern Watersheds. Multivariate regression method was used by using SPSS software. Springs average discharge was considered as the dependent variable and other affecting factors as independent variables. Two linear models were presented for estimating the alluvial and karst springs discharge. Then, the models' performance was evaluated and confirmed. Also, the artificial neural network was applied to simulate the alluvial and karst springs discharge. ANN performance was evaluated through two parameters: median root of square of the error and Pearson's R-squared statistics. The results showed that the most important factors of karst springs discharge were the porosity of aquifer formation and the site elevation; in case of the alluvial springs, the transmissivity of aquifer formation and the aquifer depth were the most important factors. Moreover, ANN efficiency in estimating springs discharge was higher than that of the multivariate regression method. [ABSTRACT FROM AUTHOR]

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Excess surface water after heavy rainfalls leads to soil erosion and flash floods, resulting in human and financial losses. Reducing runoff is an essential management tool to protect water and soil resources. This study aimed to evaluate the effects of vegetation and land management methods on runoff control and to provide a model to predict runoff values. Filed plot data and three machine learning (ML) methods, including artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIS), and extreme gradient boosting (EGB), were used in a test site in the north of Iran. In this regard, plots with various vegetation and land management treatments including bare soil treatment, rangeland cover treatment, forest litter treatment, rangeland litter treatment, tillage treatment in the direction of slope, tillage treatment perpendicular to the slope, and repetition of treatments under forest canopy were constructed on a hillslope. After each rainfall event, the amount of rainfall and corresponding runoff generated in each plot was recorded. Three ML models (ANN, CANFIS, and EGB) were used to establish relationships between amounts of recorded runoff and its controlling factors (rainfall, antecedent soil moisture (A.M.C), shrub canopy percentage and height, tree canopy percentage and height, soil texture (clay, silt, and sand percent), slope degree, leaf litter percentage of soil, and tillage interval). These data were normalized, randomized, and divided into training and testing subsets. Results showed that the ANN performed better than the other two models in predicting runoff in training (R 2  = 0.98; MSE = 0.004) and the test stages (R 2  = 0.90; MSE = 0.95). Statistical analysis and sensitivity analysis of inputs factors showed that rainfall, rangeland cover, and A.M.C are the three most important factors controlling runoff generation. The adopted method can be used to predict the effect of different vegetation and land management scenarios on runoff generation in the study area and the areas with similar settings elsewhere., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Groundwater drawdown is typically measured using pumping tests and field experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on artificial neural network (ANN)s and field measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the influence radius of each well (1000 m) were identified and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown ​​(Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was fitted to available input data to map the annual drawdown ​​across the entire aquifer. The accuracy assessment of the final map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)