Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS
نویسندگان
چکیده
Abstract Groundwater level fluctuations are one of the main components hydrogeological cycle and required variables for many water resources operation models. The numerical models can estimate groundwater (GWL) based on extensive statistics information using complex equations in any area. But most important challenges analyzing predicting depletion management is lack reliable complete data. For this reason, use artificial intelligence with high predictive accuracy due to need less data inevitable. In recent years, different has been noticed as an efficient solution. These able levels region also various field experiments such pumping tests, geophysics, soil land maps, topography slope data, boundary conditions equations. current research, first, by available statistics, Sonqor plain simulated GMS model, model evaluated two stages calibration validation. Then, much volume intelligence-based methods, GA-ANN ICA-ANN hybrid methods ELM ORELM utilized. results display that output best fit observed a correlation coefficient equal 0.96, it closest scatter points around 45 degrees line, sense, considered accurate model. To ensure correct selection Taylor diagram used. demonstrate point reference related method. Therefore, predict whole plain, instead very large time-consuming process verification, be used confidence. This approach greatly helps researchers variations dry wet years structures.
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ژورنال
عنوان ژورنال: Applied Water Science
سال: 2022
ISSN: ['2190-5495', '2190-5487']
DOI: https://doi.org/10.1007/s13201-022-01861-7