Groundwater Spring Potential Mapping Research Articles

Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors

Groundwater, a fundamental asset, isn't effectively accessible in some parts of the world. The current research work pointed toward obtaining precise maps of potential groundwater zones. This study aimed for potential groundwater modeling and extracting the precise maps using four new advanced hybrid ML models (Dagging-HP, Bagging-HP, AdaBoost-HP, Decorate-HP) and one single model Hyperpipes (HP) in the Doji Watershed, situated in the eastern part of Golestan province, Iran. Among the selected models, the AdaBoost-HP model is the most efficient, with an AUC - ROC of 0.972, accuracy (0.922), sensitivity (0.906), and specificity (0.938), which gives the most promising values, when determining the collinearity between the 14 training factors, which are, in descending order of significance, LULC, Distance to stream (DtS), Topography wetness index (TWI), HAND, Distance to the road (DtR), Geomorphology, Topography position index (TPI), Lithology, Drainage density (DD), Elevation, Slope, Rainfall, and Clay (%). The AUC-ROC approach was employed to assess the model's performance along with Accuracy, Specificity, and Sensitivity. This model revealed that 7.37% has very high groundwater potential in the eastern and south-western parts of the study, whereas 36.8% has a very low groundwater potential in the north-western and south-eastern parts of the study. It can be said from this assessment that results obtained from this investigation are better and more reliable, which gives essential encouragement for further study put on this method for groundwater potential mapping of other areas of the world along with other areas of hydrogeological investigations.

Application of machine learning to groundwater spring potential mapping using averaging, bagging, and boosting techniques

Abstract Determining groundwater potential is vital for groundwater resource management. This study aims to present a comparative analysis of three widely used ensemble techniques (averaging, bagging, and boosting) in groundwater spring potential mapping. Firstly, 12 spring-related factors and a total of 79 groundwater spring locations were collected and used as the dataset. Secondly, three typical ensemble models were adopted to predict groundwater spring potential, namely, Bayesian model averaging (BMA), random forest (RF), and the gradient boosting decision tree (GBDT). The area under the receiver operating characteristics curve (AUC) and four statistical indexes (accuracy, sensitivity, specificity, and the root mean square error (RMSE)) were used to estimate the model's accuracy. The results indicate that the three models had a good predictive performance and that the AUC values of the GBDT, RF, and BMA were 0.88, 0.84, and 0.78, respectively. Furthermore, the GBDT had the best performance (accuracy = 0.89, sensitivity = 0.91, specificity = 0.87, and RMSE = 0.33) in terms of the four indexes, followed by RF (accuracy = 0.87, sensitivity = 0.91, specificity = 0.83, and RMSE = 0.36) and BMA (accuracy = 0.76, sensitivity = 0.87, specificity = 0.65, and RMSE = 0.49). This research can provide effective guidance for using ensemble models for mapping groundwater spring potential in the future.

A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping

Limited groundwater resources and their overexploitation have become major challenges for sustainable development worldwide. In this study, an innovative hybrid approach was proposed to generate a groundwater spring potential map (GSPM) from the Sarab plain located in Lorestan Province, Iran, which includes the new best-worst method (BWM), stepwise weight assessment ratio analysis (SWARA), support vector machine learning method (SVR), Harris hawk optimization (HHO), and bat algorithms (BA). The first step involved the inventory of a map prepared to contain 610 spring locations. Randomly, 70% of the spring points were selected as training data, and the remaining 30% were selected for validation. Based on the review of the literature and available data, thirteen factors were generated as independent variables. The BWM and SWARA methods were used to identify correlations between the occurrence of springs and factors. Finally, using SVR-BA and SVR-HHO hybrid models, potential maps of groundwater springs were generated and then evaluated with receiver operating characteristic (ROC) and several statistical evaluators such as sensitivity, specificity, accuracy, and kappa index. Validation of the training data set showed that the success rates for the SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-BA, and BWM-SVR-HHO models were 92.6%, 93.7%, 95.9%, and 96.4%, respectively. The results revealed that with a small difference, BWM-SVR-HHO performed better in training compared to other models. Evaluation of the prediction rate showed that the values of the area under the ROC curve for SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-HHO, and BWM-SVR-BA were 91.7%, 92.4%, 93.3%, and 94.7%, respectively. According to the results, although all models had excellent performance with more than 90% accuracy, BWM-SVR-BA was more accurate in predicting. The hybrid models presented in this study can be used as an accurate and effective methodology to improve the results of spatial modeling of the probability of groundwater occurrence.

Groundwater spring potential assessment using new ensemble data mining techniques

The growing demand and exigency for groundwater resources warrant the demarcation of groundwater spring potential zones (GSPZ) for effective sustainable strategy in groundwater identification, conservation, and management. Here we utilized a novel data mining (DM) ensemble to generate groundwater spring potential maps (GSPMs) by combining RF-BRT (random forest-boosted regression tree), MARS-SVM (multivariate adaptive regression spline-support vector machine) and, FDA-GLM-MDA (functional data analysis-generalized linear model-mixture discriminant analysis). Initially, an aggregate of 1726 groundwater spring locations was collected from the regional water company of Tehran Province and field investigation, in which 1208 springs (70%) were taken for training purposes and the remaining 518 (30%) springs were applied for the validation process. Twelve conditioning factors including DEM (digital elevation model)/elevation, fault density, aspect, rainfall, distance from rivers, distance from faults, slope, MRVBF (multiresolution index of valley bottom flatness), TWI (topographic wetness index), lithology, land use/land cover, and permeability were utilized for mapping process and their importance in predicting the groundwater spring potential. The variable importance (VI) analysis using SVM (support vector machine) reveals that the most significant conditioning factors in the prediction process are rainfall, TWI, DEM-elevation, distance from rivers, slope, distance from faults, and MRVBF. The GSPMs generated from novel data-mining (DM) ensembles were validated using the cut-off reliant (recall, fallout, F-measure, accuracy, precision, specificity, TSS: true skill statistic, Cohen’s kappa, fourfold plot, CCI: corrected classified instances) and cut-off independent (ROC-AUC: receiver operating characteristic-area under the curve) measures. The outcome of the validation measures shows that RF-BRT has the superior values of recall, F-measure, overall accuracy, precision, specificity, TSS, Cohen’s kappa, fourfold plot, CCI followed by MARS-SVM, and FDA-GLM-MDA whereas the AUC value of RF-BRT (0.955), MARS-SVM (0.934), and FDA-GLM-MDA (0.914) also display similar result. The GSPMs generated using the novel DM ensemble models in our study can be utilized by policymakers in implementing the strategies for effective land use planning and sustainable groundwater management.

Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping

The main goal of the study was to estimate the efficiency and predictive ability of a series of tree-based ensemble methods in groundwater spring potential mapping. The study introduces a novel hybrid integration approach based on J48 Decision Trees (J48), AdaBoost (AB), Bagging (Bag), RandomSubSpace (RS), Dagging (Dag) and Rotation Forest (RF) algorithms for constructing a groundwater spring potential map. The performance of the ensemble models were evaluated at the Wuqi County in Shaanxi Province, China. At first, a groundwater spring inventory map with 235 groundwater springs was constructed. The groundwater spring database was randomly divided into a training (70% of the total number of groundwater springs) and validating subset (remaining 30%). Secondly, sixteen groundwater spring related variables were selected and analyzed, such as elevation, slope, aspect, planform and profile curvature, curvature, topographic wetness index, stream transport and stream power indexes, distance from river network, lithology, soil and landuse cover, normalized difference vegetation index, mean annual rainfall and distance from road network. The contribution of each groundwater spring related variable so as to identify the most predictive was based on the correlation attribute evaluation method. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indexes (accuracy, sensitivity, specificity). The outcomes revealed that all models had a good predictive performance, having AUC values greater than 0.74. Specifically, RF-J48 model appeared with the highest prediction capability (AUC = 0.797), followed by the AB-J48 model (0.793), the Bag-J48 (0.768), the RS-J48 (0.766) and the Dag-J48 (0.748), respectively. The study highlights that the proposed ensemble methodology is efficient and highly accurate appropriate for groundwater spring potential mapping.

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.

Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods

Water scarcity in many regions of the world has become an unpleasant reality. Groundwater appears to be one of the main natural resources capable to reverse this situation. Uncovering the spatial patterns of groundwater occurrence is a crucial factor that could assist in carrying out successful water resources management projects. The main objective of the current study was to provide a novel methodology approach which utilized Genetic Algorithm (GA) in order to perform a feature selection procedure and data mining methods for generating a groundwater spring potential map. Three data mining methods, Naïve Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF) were utilized to construct a groundwater spring potential map that had over 0.81 probability of occurrence for the Wuqi County, Shaanxi Province, China. Groundwater spring locations and sixteen related variables were analyzed, namely: lithology, soil cover, land use cover, normalized difference vegetation index (NDVI), elevation, slope angle, aspect, planform curvature, profile curvature, curvature, stream power index (SPI), stream transport index (STI), topographic wetness index (TWI), mean annual rainfall, distance from river network and distance from road network. The Frequency ratio method was used to weight the variables, whereas a multi-collinearity analysis was performed to identify the relation between the parameters and to decide about their usage. The optimal set of parameters, which was determined by the GA, reduced the number of parameters into twelve removing planform curvature, profile curvature, curvature and STI. The Receiver Operating Characteristic curve and the area under the curve (AUROC) were estimated so as to evaluate the predictive power of each model. The results indicated that the optimized models were superior in accuracy than the original models. The optimized RF model produced the best results (0.9572), followed by the optimized SVM (0.9529) and the optimized NB (0.8235). Overall, the current study highlights the necessity of applying feature selection techniques in groundwater spring assessments and also that data mining methods may be a highly powerful investigation approach for groundwater spring potential mapping.

Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization

Groundwater is one of the most beneficial natural resources worldwide. The main aim of the present study is the mapping of groundwater potential of the Zhangjiamao area in China using two novel hybrid data mining techniques that involve the adaptive neuro-fuzzy inference system (ANFIS) ensembled with teaching-learning-based optimization (TLBO) and biogeography-based optimization (BBO). First, 93 spring locations were identified in the study area, which were randomly divided in the ratio of 70:30 to be used for building models and validation, respectively. A total of 16 spring affecting factors viz., slope aspect, altitude, slope angle, plan curvature, profile curvature, curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to rivers, rainfall, lithology, soil, NDVI, and land use/land cover, were used as input variables. The probability certainty factor (PCF) method was applied for the correlation analysis between spring occurrences and the affecting factors. Subsequently, the ANFIS-TLBO and ANFIS-BBO models were generated to produce groundwater spring potential maps (GSPMs). Finally, the GSPMs were evaluated by using the area under the receiver operating characteristic (AUROC). The results showed that the AUROC values for the ANFIS-TLBO model based on the training and validation datasets were 0.866 and 0.905, respectively. In contrast, the AUROC values for the ANFIS-BBO model based on the training and validation datasets were 0.861 and 0.887, respectively. The results of ANFIS-TLBO showed that 25.66% of the study area could be classified into the high and very high groundwater spring potential classes as compared to the ANFIS-BBO model (30.52%). The results of the present study can be useful for groundwater management.

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.

Mapping of groundwater spring potential zone using geospatial techniques in the Central Nepal Himalayas: A case example of Melamchi–Larke area

Studies assessing the groundwater spring potential in the Himalayan mountain slopes are very important for sustainable water resources management and build climate resilience in mountains, but such studies are few in the Himalayas. Hence, this paper attempts to identify the groundwater spring potential zone in the Central Himalayas of Nepal. About 412 groundwater springs were surveyed, which were mainly originated from the weathered, jointed or fractured rock aquifers in the high-grade metamorphosed rocks. Eleven influencing factors, viz., altitude, slope gradient, slope shape, relative relief, flow accumulation, drainage density, geology, lineament density, land use and vegetation density were considered in assessing the groundwater spring potential using the weight of evidence method. Weight indicating the probability of groundwater spring occurrence on multiple classes of each factor was calculated and finally summed up to determine the groundwater spring potential. Gentle slope, low relative relief, high flow accumulation, north- and east-facing slopes, denser lineament density, altitude class of 1500–2500 m, high vegetation density, and forest demonstrated a higher likelihood of spring occurrence. Validation of the groundwater spring potential map was successful, which implies the method can be replicated in a similar biophysical environment, where the hydrogeological or geophysical surveyed data is not available.

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Abstract. Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models

The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.

A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models

The main aim of this study is to generate groundwater spring potential maps for the Ningtiaota area (China) using three statistical models namely statistical index (SI), index of entropy (IOE) and certainty factors (CF) models. Firstly, 66 spring locations were identified by field surveys, out of which, 46 (70%) spring locations were randomly selected for training the models and the rest 20 (30%) spring locations were used for validation. Secondly, 12 spring influencing factors, namely slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to streams, lithology and normalized difference vegetation index (NDVI) were derived from the spatial database. Subsequently, using the mentioned factors and the three models, groundwater spring potential values were calculated and the results were plotted in ArcGIS 10.0. Finally, the area under the curve was used to validate groundwater spring potential maps. The results showed that the IOE model, with the highest success rate of 0.9126 and the highest prediction rate of 0.9051, showed the preferable performance in this study. The results of this study may be helpful for planners and engineers in groundwater resource management and other similar watersheds.

GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran

This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan curvature, profile curvature, slope length, topographic wetness index, distance from faults, distance from rivers, river density, fault density, land use, and lithology. Then, frequency ratio and boosted regression tree models were applied and groundwater potential maps (GPMs) were produced. In the last step, validation of the models was carried out implementing receiver operating characteristics (ROC) curve. According to the results, BRT had area under curve of ROC (AUC-ROC) of 87.2%, while it was seen that FR had AUC-ROC of 83.2% that implies acceptable operation of the models. According to the results of this study, topographic wetness index was the most important factor, followed by altitude, and distance from rivers. On the other hand, aspect, and plan curvature were seen to be the least important factors. The methodology implemented in this study could be used for other basins with similar conditions to cope with water resources problem.

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities.

GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

This study evaluated and compared groundwater spring potential maps produced with two different models—namely multivariate adaptive regression spline (MARS) and random forest (RF)—using geographic information system (GIS). In total, 234 spring locations were identified in the Boujnord, North Khorasan, Iran and a GIS spring inventory map was prepared. Of these, 176 (70 %) locations were employed to produce spring potential maps (training), while the remaining 58 (30 %) cases were used to validate the model. The explanatory variables used to predict spring location were altitude, slope aspect, slope degree, slope length, topographic wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance to rivers, drainage density, distance to faults, and fault density. Furthermore, the spatial relationships between spring occurrence and explanatory variables were performed using a Certainty Factor (CF) model. For validation, area under a receiver operating characteristics (ROC) curves (AUC) was used. The validation results showed that the AUC for calibration is almost identical (0.79) in both models, while for prediction, the MARS model (73.26 %) performed better than RF (70.98 %) model. These results indicate that the MARS and RF models are good estimators of groundwater spring potential in the study area. These groundwater spring potential maps can be applied to groundwater management and groundwater resource exploration.

GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran

Three statistical models—frequency ratio (FR), weights-of-evidence (WofE) and logistic regression (LR)—produced groundwater-spring potential maps for the Birjand Township, southern Khorasan Province, Iran. In total, 304 springs were identified in a field survey and mapped in a geographic information system (GIS), out of which 212 spring locations were randomly selected to be modeled and the remaining 92 were used for the model evaluation. The effective factors—slope angle, slope aspect, elevation, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, lithology, land use, and distance to river, road, fault—were derived from the spatial database. Using these effective factors, groundwater spring potential was calculated using the three models, and the results were plotted in ArcGIS. The receiver operating characteristic (ROC) curves were drawn for spring potential maps and the area under the curve (AUC) was computed. The final results indicated that the FR model (AUC = 79.38 %) performed better than the WofE (AUC = 75.69 %) and LR (AUC = 63.71 %) models. Sensitivity and factor analyses concluded that the bivariate statistical index model (i.e. FR) can be used as a simple tool in the assessment of groundwater spring potential when a sufficient number of data are obtained.

Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran

The aim of the current study was to produce groundwater spring potential map using a bivariate statistical model (frequency ratio) and geographical information system (GIS) in the Taleghan Watershed, Alborz Province, Iran. Firstly, field surveys were done for identifying and springs inventory mapping. In total, 457 springs were identified and mapped in GIS; out of that, 320 (70 %) locations were selected for training and the remaining 137 (30 %) cases were used for the model validation. The effective factors on the groundwater spring such as: slope percent, slope aspect, altitude, topographic wetness index, stream power index, slope length, plan curvature, distance from rivers, distance from roads, distance from faults, lithology, land use, soil hydrology groups, and drainage density were derived from the spatial database. Using the above effective factors, groundwater spring potential mapping was calculated using FR model as a bivariate statistical model, and the results were plotted in Arc GIS. Eventually, the receiver operating characteristic curve was drawn for spring potential map and the area under the curve (AUC) was figured. Validation of results indicated that the frequency ratio model (AUC = 75.99 %) performed fairly good predication accuracy. The results of groundwater spring potential map may be helpful for planners and engineers in water resource management and land use planning.