scholarly journals Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 658
Author(s):  
Sadegh Karimi-Rizvandi ◽  
Hamid Valipoori Goodarzi ◽  
Javad Hatami Afkoueieh ◽  
Il-Moon Chung ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Groundwater Resources ◽  
Slope Angle ◽  
Drainage Density ◽  
Groundwater Potential ◽  
Estimation Accuracy ◽  
Slope Aspect ◽  
Khuzestan Province ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

scholarly journals Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)

2020 ◽  
Vol 12 (3) ◽  
pp. 490 ◽  
Author(s):  
Alireza Arabameri ◽  
Saro Lee ◽  
John P. Tiefenbacher ◽  
Phuong Thao Thi Ngo
Keyword(s):  
Groundwater Resources ◽  
Groundwater Potential ◽  
Rate Curve ◽  
Yield Potential ◽  
Validation Dataset ◽  
Well Yield ◽  
Potential Map ◽  
Potential Mapping ◽  
Groundwater Potential Map ◽  
Groundwater Potential Mapping

The aim of this research is to introduce a novel ensemble approach using Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), and random forest (RF) models for groundwater-potential mapping (GWPM) in Bastam watershed, Iran. This region suffers from freshwater shortages and the identification of new groundwater sites is a critical need. Remote sensing and geographic information system (GIS) were used to reduce time and financial costs of rapid assessment of groundwater resources. Seventeen physiographical, hydrological, and geological groundwater conditioning factors (GWCFs) were derived from a spatial geo-database. Groundwater data were gathered in field surveys and well-yield data were acquired from the Iranian Department of Water Resources Management for 89 locations with high yield potential values ≥ 11 m3 h−1. These data were mapped in a GIS. From these locations, 62 (70%) were randomly selected to be used for model training, and the remaining 27 (30%) were used for validation of the model. The relative weights of the GWCFs were determined with an RF model. For GWPM, 220 randomly selected points in the study area and their final weights were determined with the VIKOR model. A groundwater potential map was created by interpolating the values at these points using Kriging in GIS. Finally, the area under receiver operating characteristic (AUROC) curve was plotted for the groundwater potential map. The success rate curve (SRC) was computed for the training dataset, and the prediction rate curve (PRC) was calculated for the validation dataset. Results of RF analysis show that land use and land cover, lithology, and elevation are the most significant determinants of groundwater occurrence. The validation results show that the ensemble model had excellent prediction performance (PRC = 0.934) and goodness-of-fit (SRC = 0.925) and reasonably high classification accuracy. The results of this study could aid management of groundwater resources and assist planners and decision makers in groundwater-investment planning to achieve sustainability.

Integrated Geomorphological, Geospatial and AHP Technique for Groundwater Prospects Mapping in Basaltic Terrain

2018 ◽  
Vol 2 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Vaishnavi Mundalik ◽  
Clinton Fernandes ◽  
Ajaykumar Kadam ◽  
Bhavana Umrikar
Keyword(s):  
Groundwater Resources ◽  
Drainage Density ◽  
Groundwater Potential ◽  
The Sustainable Development ◽  
Thematic Layers ◽  
Groundwater Availability ◽  
Basaltic Terrain ◽  
Thematic Layer ◽  
Increasing Demand ◽  
Gis Environment

Groundwater is an important source of drinking water in rural parts of India. Because of the increasing demand for water, it is essential to identify new sources for the sustainable development of this resource. The potential mapping and exploration of groundwater resources have become a breakthrough in the field of hydrogeological research. In the present paper, a groundwater prospects map is delineated for the assessment of groundwater availability in Kar basin on basaltic terrain, using remote sensing and Geographic Information System (GIS) techniques. Various thematic layers such as geology, slope, soil, geomorphology, drainage density and rainfall are prepared using satellite data, topographic maps and field data. The ranks and weights were assigned to each thematic layer and various categories of those thematic layers using AHP technique respectively. Further, a weighted overlay analysis was performed by reclassifying them in the GIS environment to prepare the groundwater potential map of the study area. The results show that groundwater prospects map classified into three classes low, moderate and high having area 17.12%, 38.26%, 44.62%, respectively. The overlay map with the groundwater potential zones in the study area has been found to be helpful for better planning and managing the resources.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

scholarly journals Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Sunil Saha ◽  
Amiya Gayen ◽  
Kaustuv Mukherjee ◽  
Hamid Reza Pourghasemi ◽  
M. Santosh
Keyword(s):  
Machine Learning ◽  
Influencing Factors ◽  
Groundwater Resources ◽  
Learning Algorithms ◽  
Drainage Density ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Lineament Density ◽  
Dug Wells ◽  
Groundwater Potentiality

Abstract Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

Mapping of Groundwater potential zones in Lingasugur Taluk in North-eastern part of Karnataka, India using Remote Sensing, GIS and multi-criteria data analysis

2021 ◽  
Vol 14 (12) ◽  
pp. 13-22
Author(s):  
Ajgaonkar Swanand ◽  
S. Manjunatha
Keyword(s):  
Remote Sensing ◽  
Groundwater Resources ◽  
Drainage Density ◽  
Groundwater Potential ◽  
Groundwater Exploration ◽  
Geographical Information ◽  
Groundwater Potential Zones ◽  
Thematic Maps ◽  
North Eastern

Groundwater research has evolved tremendously as presently it is the need of society. Remote Sensing (RS) and Geographical Information System (GIS) are the main methods in finding the potential zones for the groundwater. They help in assessing, exploring, monitoring and conserving groundwater resources. A case study was conducted to find the groundwater potential zones in Lingasugur taluk, Raichur District, Karnataka State, India. Ten thematic maps were prepared for the study area such as geology, hydrogeomorphology, land use/ land cover, soil type, NDVI, NDWI, slope map, lineament density, rainfall and drainage density. A weighted overlay superimposed method was used after converting all the thematic maps in raster format. Thus from analysis, the classes in groundwater potential were made as very good, moderate, poor and very poor zones covering an area of 10.1 sq.km., 169.25 sq.km., 1732.31 sq.km. and 53.66 sq.km. respectively. By taking the present study into consideration, the future plans for urbanization, recharge structures and groundwater exploration sites can be decided.

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