Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran

Golkarian, Ali; Rahmati, Omid

doi:10.1007/s12665-018-7551-y

Cited by 49 publications

(16 citation statements)

References 99 publications

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“…During the validation phase, performances of the models that indicate their capabilities to estimate groundwater potential [37] were decreased to a mean AUC of 0.73. Based on the relationship between AUC values and the predictive capability of the models that has been suggested in the literature [14,19,22,24,84], we can conclude that our models performed decently in estimating groundwater potential and developing distribution maps. Further, the results demonstrated the capability of ensemble learning techniques for improving LR performance.…”

Section: Discussionmentioning

confidence: 60%

“…Rainfall is one of the most important factors for groundwater potential mapping as it directly affects groundwater recharge [10,12,22]. The yearly average rainfall of this area varies from 4.80 to 7.23 mm (Figure 2j).…”

Section: Groundwater Influencing Factorsmentioning

confidence: 99%

“…Machine learning methods and GIS-based data processing techniques have provided opportunities for developing powerful tools for the assessment of groundwater potential in a time-and cost-effective manner. Examples of these methods include a classification and regression tree (CART) [14], logistic model tree (LMT) [11], multivariate adaptive regression spline (MARS) [15], random forest (RF) [16], boosted regression tree (BRT) [17], maximum entropy (ME) [18], support vector machine (SVM) [19,20], artificial neural network (ANN) [20], multivariate adaptive regression [21], and maximum entropy [22].…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

Avand

et al. 2020

Applied Sciences

142

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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.

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Section: Discussionmentioning

confidence: 60%

Section: Groundwater Influencing Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

Avand

et al. 2020

Applied Sciences

142

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“…Several researchers globally have used different techniques to identify potential groundwater recharge zones such as frequency ratio (Al-Abadi, Al-Temmeme, & Al-Ghanimy, 2016;Balamurugan et al, 2017Das & Pardeshi, 2018bGuru, Seshan, & Bera, 2017;Ozdemir, 2011;Naghibi et al, 2015a,b;Razandi, Pourghasemi, Neisani, & Rahmati, 2015). Logistic regression model techniques (Ozdemir, 2011;Pourtaghi & Pourghasemi, 2014), random forest model (Golkarian & Rahmati, 2018;Naghibi, Pourghasemi, & Dixon, 2016), decision tree model (Chenini, Mammou, &El May, 2010, Lee andJones-Lee, 1999); artificial neural network (Naghibi, Pourghasemi, & Abbaspour, 2018), and evidential belief function (Nampak, Pradhan, & Manap, 2014).…”

Section: Introductionmentioning

confidence: 99%

Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP)

Dar

Rai

Bhat

2020

Geology, Ecology, and Landscapes

174

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In the present study, an effort was made in Kashmir Valley, NW-Himalayas to delineate potential groundwater areas using GIS and Analytical Hierarchy method (AHP) by incorporating remote sensing data with data from other sources. These data sets were used to prepare eight thematic layers of slope, soil texture, drainage density, land/land cover (LULC), lithology, geomorphology, lineament density, and rainfall. The analytical method of AHP pair wise matrix was used to evaluate the normalized weight of these thematic layers. All the thematic layers and their corresponding classes were allocated ranks and weights based on their impact on groundwater potential. The study area has been classified into five different potential groundwater recharge zonesexcellent (28.97%), good (19.99%), moderate (21.70%), poor (27.16%) and very poor (2.15%). The groundwater potential map prepared for the study area has been validated with 245 existing bore-wells and flow direction of basin. The validation of results was in agreement with the evidence obtained as Area Under Curve (AUC) was calculated to be 79.69% and flow computational investigations which suggest groundwater flows north-eastward from south of basin up to central Kashmir, and south-westward from North towards Jhelum River. The validation suggests that the applied method proves to give us a very significant and reliable result for the study area.

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“…The rapid development of probabilistic, machine learning, data mining, and ensemble models in recent decades is enhancing the basement to determine groundwater recharge opportunity, soil erosion susceptibility, gully erosion susceptibility, and other spatial modelings. Some new methods which were used by the researcher for spatial hazards probability and groundwater potentiality modeling are: evidential belief function (EBF), weights of evidence (WoE), frequency ratio (FR), classification and regression tree (CART,), boosted regression tree (BRT), decision tree (DT), artificial neural network (ANN), multivariate adaptive regression splines (MARS), binary logistic regression (BLR), Shannon's entropy (SE), analytic hierarchy process (AHP), maximum entropy (ME), random forest (RF), fuzzy logic (FL), support vector machine (SVM), multi-criteria decision analysis (MDCA), logistic model tree (LMT), quadratic discriminate analysis (QDA), K-nearest neighbor (KNN), and certainty factor (CF) [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.

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Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran

Cited by 49 publications

References 99 publications

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

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

Delineation of potential groundwater recharge zones using analytical hierarchy process (AHP)

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

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