Universiti Teknologi Malaysia Institutional Repository

A hybrid computational intelligence approach to groundwater spring potential mapping

Bui, Dieu Tien and Shirzadi, Ataollah and Chapi, Kamran and Shahabi, Himan and Pradhan, Biswajeet and Pham, Binh Thai and Singh, Vijay P. and Chen, Wei and Khosravi, Khabat and Ahmad, Baharin and Lee, Saro (2019) A hybrid computational intelligence approach to groundwater spring potential mapping. Water (Switzerland), 11 (10). p. 2013. ISSN 2073-4441

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Official URL: http://dx.doi.org/10.3390/w11102013

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 singleADTree, 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.

Item Type:Article
Uncontrolled Keywords:Over-fitting, Performance
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.212-70.215 Geographic information system
Divisions:Built Environment
ID Code:89489
Deposited By: Widya Wahid
Deposited On:22 Feb 2021 09:47
Last Modified:22 Feb 2021 09:47

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