Search

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.

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]

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]

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.

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.)