scholarly journals Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

2020 ◽  
Vol 12 (7) ◽  
pp. 2622 ◽  
Author(s):  
Phong Tung Nguyen ◽  
Duong Hai Ha ◽  
Huu Duy Nguyen ◽  
Tran Van Phong ◽  
Phan Trong Trinh ◽  
...  
Keyword(s):  
Decision Trees ◽  
Flow Direction ◽  
Groundwater Resources ◽  
Groundwater Potential ◽  
Hybrid Models ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Statistical Measures ◽  
The World ◽  
Potential Mapping

Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.

scholarly journals Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1909 ◽  
Author(s):  
Kalantar ◽  
Al-Najjar ◽  
Pradhan ◽  
Saeidi ◽  
Halin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Groundwater Potential ◽  
Environmental Modeling ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

scholarly journals Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

2020 ◽  
Vol 10 (7) ◽  
pp. 2469 ◽  
Author(s):  
Phong Tung Nguyen ◽  
Duong Hai Ha ◽  
Mohammadtaghi Avand ◽  
Abolfazl Jaafari ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Soft Computing ◽  
Predictive Value ◽  
Flow Direction ◽  
Groundwater Potential ◽  
Topographic Wetness Index ◽  
Ensemble Techniques ◽  
Well Yield ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

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 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.

scholarly journals The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

2021 ◽  
Vol 13 (5) ◽  
pp. 2459
Author(s):  
Soyoung Park ◽  
Jinsoo Kim
Keyword(s):  
Groundwater Resources ◽  
Management Strategies ◽  
Management Plan ◽  
Groundwater Potential ◽  
Gradient Boosting ◽  
Stream Power ◽  
Area Index ◽  
Conditioning Factors ◽  
Extreme Gradient Boosting ◽  
Stream Power Index

Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

Optimization of Statistical and Machines Learning Hybrid Models for Groundwater Potential Mapping

2021 ◽  
pp. 1-34
Author(s):  
Peyman Yariyan ◽  
Mohammadtaghi Avand ◽  
Ebrahim Omidvar ◽  
Quoc Bao Pham ◽  
Nguyen ThiThuy Linh ◽  
...  
Keyword(s):  
Groundwater Potential ◽  
Hybrid Models ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

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

2018 ◽  
Vol 22 (9) ◽  
pp. 4771-4792 ◽  
Author(s):  
Khabat Khosravi ◽  
Mahdi Panahi ◽  
Dieu Tien Bui
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Spatial Prediction ◽  
Bees Algorithm ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Inference System ◽  
Conditioning Factors ◽  
Neuro Fuzzy ◽  
Potential Mapping

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.

scholarly journals Groundwater Site Prediction Using Remote Sensing, GIS and Statistical Approaches: A Case Study in the Western Desert, Iraq

2018 ◽  
Vol 7 (4.20) ◽  
pp. 166 ◽  
Author(s):  
Fadhil M. Shnewer ◽  
Alauldeen A. Hasan ◽  
Mudhaffar S. AL-Zuhairy
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Relationship ◽  
Groundwater Potential ◽  
Western Desert ◽  
Geographical Information ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Combination of remote sensing data and geographical information system (GIS) for the investigation of groundwater has become an advance approach in the researches of groundwater. The purpose of this research is to apply statistical models such as Evidential Belief Function (EBF) and Logistic Regression (LR) for mapping groundwater potential sites at Iraqi western desert (located at Al-Ramadi and Shithatha). The potential of the groundwater areas were determined depending on the spatial relationship between groundwater wells and different conditioning factors. These factors include altitude, curvature, aspect, slope, soil, normalized difference vegetation index (NDVI), topographic wetness index, fault, rainfall, stream density, stream power index, and lithology. The algorithms were used to model all layers of groundwater conditioning factors to generate groundwater probability areas. Then, the final maps included five potential classes i.e., very high, high, moderate, low and very low susceptible zones were generated. The final outcomes were validated using Area Under the Curve (AUC) algorithm. The values of success rates were 76.5% and 71.5% for EBF an LR respectively. The prediction rates for the same methods were 73.7% and 70%, respectively.  The thematic maps attained from the present study indicated the capability of EBF and LR methods in groundwater potential mapping.  

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