Abstract
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.
Highlights
Groundwater serves as the source of water supply needed for different sectors, including agriculture, industry, animal husbandry, and communities in many countries around the world [1,2].Groundwater is often the result of infiltration of rainwater, snowmelt water into soil and underlying rocks, and thereupon fills the pore space of soil and rocks [3,4]
Groundwater Spring Conditioning Factor Analysis. Both models and input data affect the quality of groundwater spring potential mapping (GSPM) results [15]
The main step in the spatial GSPM is the selection of suitable factors and the elimination of
Summary
Groundwater serves as the source of water supply needed for different sectors, including agriculture, industry, animal husbandry, and communities in many countries around the world [1,2].Groundwater is often the result of infiltration of rainwater, snowmelt water into soil and underlying rocks, and thereupon fills the pore space of soil and rocks [3,4]. Since groundwater consumption in Iran has been increasing dramatically, development of proper methods to assess the aquifer productivity and groundwater potential areas are badly needed. These methods are essential for future systematic development, profitable management, and arresting the decline of groundwater resources [13]. Groundwater spring potential mapping (GSPM) is important for protecting water quality and managing the use of groundwater [14]. GSPM is useful for proper groundwater protection and management [15]
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