Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

被引：0
作者：
Saeideh Samani
Meysam Vadiati
Farahnaz Azizi
Efat Zamani
Ozgur Kisi
机构：
[1] Water Research Institute (WRI),Department of Water Resources Study and Research
[2] Hubert H. Humphrey Fellowship Program,Department of Civil Engineering
[3] Global Affairs,Department of Civil Engineering
[4] University of California,undefined
[5] Kohgiluyeh and Boyerahmad Regional Water Company,undefined
[6] Iranian Water Resources Management Company (WRM),undefined
[7] University of Applied Sciences,undefined
[8] Ilia State University,undefined
来源：
Water Resources Management | 2022年 / 36卷
关键词：
Soft computing; Groundwater level prediction; Hydrogeology; Meteorological components;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management. Compared with alternative numerical models, soft computing methods are promising tools for GWL prediction, which need more hydrogeological and aquifer characteristics. The central aim of this research is to explore the performance of such well-accepted data-driven models to predict monthly GWL with emphasis on major meteorological components, including; precipitation (P), temperature (T), and evapotranspiration (ET). Artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least-square support vector machine (LSSVM) are used to predict one-, two-, and three-month ahead GWL in an unconfined aquifer. The main meteorological components (Tt, ETt, Pt, Pt-1) and GWL for one, two, and three lag-time (GWLt-1, GWLt-2, GWLt-3) are used as input to attain precise prediction. The results show that all models could have the best prediction for one month ahead in scenario 5, comprising inputs of GWLt-1, GWLt-2, GWLt-3, Tt, ETt, Pt, Tt-1, ETt-1, Pt-1. Based on different evaluation criteria, all employed models could predict the GWL with a desirable accuracy, and the results of LSSVM are the superior one.
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页码：3627 / 3647
页数：20
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