Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

Naghibi, Seyed Amir; Moradi Dashtpagerdi, Mostafa

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features | Evaluation de quatre méthodes d’apprentissage supervisé pour la cartographie du potentiel des sources d’eaux souterraines dans la région de Khalhal (Iran) à partir des fonctionnalités d’un SIG Evaluación de cuatro métodos de aprendizaje supervisado para el mapeo de potenciales manantiales de agua subterránea en la región Khalkhal (Irán) utilizando características basadas en GIS 采用基于GIS特点评估(伊朗)Khalkhal地区地下水泉潜力绘图四种监管的学习方法 Avaliação de quatro métodos de aprendizagem supervisionada para o mapeamento do potencial de nascentes de águas subterrâneas na região de Khalkhal (Irã) através de ferramentas baseadas em SIG

2017

Naghibi, Seyed Amir | Moradi Dashtpagerdi, Mostafa

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

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