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"Groundwater potential"
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Groundwater vulnerability assessment and mapping using DRASTIC model
This book shows the effectiveness of DRASTIC model in a geographical setting for validation of vulnerable zones and presents the optimization of parameters for the development of precise maps highlighting several zones with varied contamination. Impact of vadose zone has also been assessed by considering every sub-surface layer.
Book
Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS
This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.
Journal Article
Simulating Groundwater Potential Zones in Mountainous Indian Himalayas—A Case Study of Himachal Pradesh
Groundwater resources are increasingly important as the main supply of fresh water for household, industrial, and agricultural activities. However, overuse and depletion of these resources can lead to water scarcity and resource deterioration. Therefore, assessing groundwater availability is essential for sustainable water management. This study aims to identify potential groundwater zones in the Bilaspur district of Himachal Pradesh using the Multi Influencing Factor (MIF) technique, a modern decision-making method widely used in various sectors. Geospatial models were integrated with the MIF technique to evaluate prospective groundwater areas. Grid layouts of all underground water influencing variables were given a predetermined score and weight in this decision-making strategy. The potential groundwater areas were then statistically assessed using graded data maps of slope, lithology, land-use, lineament, aspect, elevation, soil, drainage, geomorphology, and rainfall. These maps were converted into raster data using the raster converter tool in ArcGIS software, utilizing Survey of India toposheets, SRTM DEM data, and Resourcesat-2A satellite imageries. The prospective groundwater zones obtained were classified into five categories: nil–very low, covering 0.34% of the total area; very low–low (51.64%); low–moderate (4.92%); moderate–high (18%) and high–very high (25%). Scholars and policymakers can collaborate to develop systematic exploration plans for future developments and implement preservative and protective strategies by identifying groundwater recharge zones to reduce groundwater levels. This study provides valuable insights for long-term planning and management of water resources in the region.
Journal Article
Delineation of Potential Groundwater Zones Using GIS-based Fuzzy AHP Technique for Urban Expansion in the Southwestern Fringe of Guwahati City, India
Due to unprecedented urban growth many localities within the heart of Guwahati city witness groundwater scarcity, mainly during the dry seasons. This study aims to identify potential groundwater zones in the southwestern fringe of the city where the Guwahati Metropolitan Development Authority (GMDA) has adopted plans for future expansion. Rani and Chayani Barduar are two administrative blocks adjacent to the city, possessing a vast tract of unsettled agricultural land ideal for future township development. Multi-criteria decisionmaking technique using a Fuzzy Analytical Hierarchy Process (FAHP) in a Remote Sensing and Geographic Information System (GIS) environment is used to produce the groundwater potential map. A total of eight thematic layers important for groundwater recharge: lithology, geomorphology, slope, rainfall, lineament density, soil, drainage density, and Land Use Land Cover are prepared using satellite data, fieldwork, and other suitable techniques and used as input. The study area is classified into five groundwater potential zones – very high (42.52 %), high (28.67 %), moderate (17.23%), poor (10.21 %), and very poor (1.37%). Validation of the result using a yield map derived from the exploratory wells of the Central Ground Water Board (CGWB) shows strong agreement with the prediction accuracy (AUC = 73.36%). Field-derived water level data also show a high negative correlation (R2 = 0.71) with yield data indicating high specific yield in wells with shallow water levels. The study results will help planners and policymakers with future urban development strategies and sustainable groundwater management practices.
Journal Article
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Due to the rapidly increasing demand for groundwater, as one of the principal freshwater resources, there is an urge to advance novel prediction systems to more accurately estimate the groundwater potential for an informed groundwater resource management. Ensemble machine learning methods are generally reported to produce more accurate results. However, proposing the novel ensemble models along with running comparative studies for performance evaluation of these models would be equally essential to precisely identify the suitable methods. Thus, the current study is designed to provide knowledge on the performance of the four ensemble models i.e., Boosted generalized additive model (GamBoost), adaptive Boosting classification trees (AdaBoost), Bagged classification and regression trees (Bagged CART), and random forest (RF). To build the models, 339 groundwater resources’ locations and the spatial groundwater potential conditioning factors were used. Thereafter, the recursive feature elimination (RFE) method was applied to identify the key features. The RFE specified that the best number of features for groundwater potential modeling was 12 variables among 15 (with a mean Accuracy of about 0.84). The modeling results indicated that the Bagging models (i.e., RF and Bagged CART) had a higher performance than the Boosting models (i.e., AdaBoost and GamBoost). Overall, the RF model outperformed the other models (with accuracy = 0.86, Kappa = 0.67, Precision = 0.85, and Recall = 0.91). Also, the topographic position index’s predictive variables, valley depth, drainage density, elevation, and distance from stream had the highest contribution in the modeling process. Groundwater potential maps predicted in this study can help water resources managers and policymakers in the fields of watershed and aquifer management to preserve an optimal exploit from this important freshwater.
Journal Article
Identification of Groundwater Potential Zones Using Remote Sensing and GIS Techniques: A Case Study of the Shatt Al-Arab Basin
Rapid population growth has raised the groundwater resources demand for socio-economic development in the Shatt Al-Arab basin. The sustainable management of groundwater resources requires precise quantitative evaluation, which can be achieved by applying scientific principles and modern techniques. An integrated concept has been used in the current study to identify the groundwater potential zones (GWPZs) in the Shatt Al-Arab basin using remote sensing (RS), geographic information system (GIS), and analytic hierarchy process (AHP). For this purpose, nine groundwater occurrence and movement controlling parameters (i.e., lithology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover, distance to river, and lineament density) were prepared and transformed into raster data using ArcGIS software. These nine parameters (thematic layers) were allocated weights proportional to their importance. Furthermore, the hierarchical ranking was conducted using a pairwise comparison matrix of the AHP in order to estimate the final normalized weights of these layers. We used the overlay weighted sum technique to integrate the layers for the creation of the GWPZs map of the study area. The map has been categorized into five zones (viz., very good, good, moderate, poor, and very poor) representing 4, 51, 35, 9, and 1% of the study area, respectively. Finally, for assessing the effectiveness of the model, the GWPZs map was validated using depth to groundwater data for 99 wells distributed over the basin. The validation results confirm that the applied approach provides significantly solid results that can help in perspective planning and sustainable utilization of the groundwater resources in this water-stressed region.
Journal Article
Spatial and decision-making approaches for identifying groundwater potential zones: a review
Effective assessment of any region's groundwater resources depends greatly on the levels of the sub-surface water. Since groundwater resources are being overused, the availability of groundwater is in a critical scenario. Quality of the groundwater is deteriorating in numerous regions as a result of the worrisome rate of groundwater table depletion. Depending on how frequently the aquifer under the earth surface is recharged by surface water supplies, groundwater can be kept underground for days, weeks, months, years, centuries, or even millennia. Currently, the utility is increased as compared to availability. The current water demand exceeds the surface water supply. As a result, for the effective management and usage of the priceless natural resources, groundwater potential zones’ systematic evaluation is now essential. The understanding about monitoring and a suitable sustainable development strategy for water resources is provided by groundwater potential zoning. The delineation of groundwater potential zoning is influenced by various factors, including rainfall, land-use cover, geological formations, geomorphology, drainage features, slope, etc. To ensure the sustainable groundwater management in the basin, it is essential to locate groundwater potential zones, so that series of recharge structures may be built there to manage aquifer recharge. Remote sensing and GIS are recent techniques that become very crucial tools in accessing, monitoring, and conserving groundwater resources because of their advantages of spatial, spectral, and temporal availability and interpolation of data covering big and inaccessible areas in short amount of time.
Journal Article
Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia
The objective of this paper is to exploit the potential application of weighted index overlay analysis for assessing groundwater potential mapping at Dhungeta-Ramis sub-basin, Wabi Shebele basin, Ethiopia using remote sensing and geographic information system (GIS) technique. For this purpose, seven groundwater occurrences and movement controlling factors, including, lithology, slope, land use land cover (LULC), rainfall, lineaments, soil, and drainage density were mapped. Then, weight was assigned to thematic maps, and the groundwater prospective of the sub-basin is qualitatively classified into five classes, namely, very good, good, moderate, poor, and very poor which account for 2.22%, 26.93%, 56.74%, 13.84%, and 0.26% landscape, respectively. The cross-validation of the resultant model was carefully carried out using spring, hand-dug, and deep well data. The result reveals that 89% of springs were overlaying good and/or very good groundwater potential zones and 58% of deep well shows the same scenario, whereas 42% of deep well overlays moderate zone. As a result, the map generated using this platform could be used as a preliminary reference in selecting suitable sites for groundwater resource exploitation.
Journal Article
Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach
Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier ensemble method, namely Random Forest Classifier based on Random Subspace Ensemble (RS-RF), for groundwater potential mapping (GWPM) in Qorveh-Dehgolan plain, Kurdistan province, Iran. A total of 12 conditioning factors (slope, aspect, elevation, curvature, stream power index (SPI), topographic wetness index (TWI), rainfall, lithology, land use, normalized difference vegetation index (NDVI), fault density, and river density) were selected for groundwater modeling. The least square support vector machine (LSSVM) feature selection method with a 10-fold cross-validation technique was used to validate the predictive capability of these conditioning factors for training the models. The performance of the RS-RF model was validated using the area under receiver operating characteristic curve (AUROC), success and prediction rate curves, kappa index, and several statistical index-based measures. In addition, Friedman and Wilcoxon signed-rank tests were used to assess statistically significant level among the new model with the state-of-the-art soft computing benchmark models, such as random forest (RF), logistic regression (LR) and naïve Bayes (NB). Results showed that the new hybrid model of RS-RF had a very high predictive capability for groundwater potential mapping and exhibited the best performance among other benchmark models (LR, RF, and NB). Results of the present study might be useful to water managers to make proper decisions on the optimal use of groundwater resources for future planning in the critical study area.
Journal Article
Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping
The main goal of this study was to optimize an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms—genetic algorithm (GA), biogeography-based optimization (BBO) and simulated annealing (SA)—to prepare groundwater potential maps. The methodology was applied to the Booshehr plain, Iran. The results of optimized models were compared with ANFIS individually and three bivariate models: frequency ratio (FR), evidential belief function (EBF), and the entropy model. First, 339 wells with groundwater yield higher than 11 m3/h were selected and randomly divided into two groups. In all, 238 wells (70%) were used for training the models and 101 wells (30%) were used for testing and validating the models. Fifteen conditioning factors were selected as input parameters for the modeling. The accuracy of the groundwater potential maps for the study area was determined using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation of error (SD), as well as the area under the receiver operating characteristic (ROC) curve (AUC). Overall, the results demonstrated that ANFIS-GA had the highest prediction capability (AUC = 0.915) for groundwater potential mapping followed by ANFIS-BBO (0.903), entropy (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) and EBF (0.80). According to the entropy model, land-use, soil order and rainfall factors had the highest impact on groundwater potential in the study area. The results of this research show that the ANFIS models combined with meta-heuristic optimization algorithms can be a useful decision-making tool for assessment and management of groundwater resources.
Journal Article