TY - JOUR ID - 85220 TI - Predicting Groundwater Potential Areas Using Hybrid Artificial Intelligence Methods (Case Study: Birjand Plain) JO - Iranian Journal of Soil and Water Research JA - IJSWR LA - en SN - 2008-479X AU - Eftekhari, Mobin AU - Eslaminezhad, Seyed Ahmad AU - Haji Elyasi, Ali AU - Akbari, Mohammad AD - MSc. graduate, Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran AD - MSc. graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran AD - Department of Civil Engineering, K. N. Toosi University of Technology, Tehran,Iran AD - Dept. of Civil Engineering, University of Birjand, Birjand, Iran Y1 - 2021 PY - 2021 VL - 52 IS - 9 SP - 2383 EP - 2397 KW - Groundwater Potential KW - Artificial Intelligence KW - Semi-arid areas DO - 10.22059/ijswr.2021.325397.669001 N2 - Groundwater is one of the most valuable resources for communities, agriculture, and industry. In the present study, three new artificial intelligence models, including Modified Real AdaBoost (MRAB), Bagging model (BA), and Rotation Forest model (RF), have been developed by the Functional Tree Base Classifier (FT) model to predict groundwater potential in Birjand plain area. Therefore, for implementation, geo-hydrological data of 37 groundwater wells and ten factors of topography, hydrology, and geology were used. The performance of these models was evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that although all the hybrid models developed in this study increased the prediction accuracy, MRAB-FT model (AUC = 0.742) has higher accuracy in predicting potential groundwater areas in Birjand plain. Accurate mapping of groundwater potential areas while maintaining a balance between consumption and operation will help feed the aquifer for optimal use of groundwater resources. UR - https://ijswr.ut.ac.ir/article_85220.html L1 - https://ijswr.ut.ac.ir/article_85220_d26c98c865dd4646a6e51f6eb231208d.pdf ER -