Evaluating machine learning methods for predicting groundwater fluctuations using GRACE satellite in arid and semi-arid regions  

作　　者：Mobin Eftekhari Abbas Khashei-Siuki 

机构地区：[1]Department of Water Engineering,University of Birjand,Birjand,Iran

出　　处：《Journal of Groundwater Science and Engineering》2025年第1期5-21,共17页地下水科学与工程(英文版)

摘　　要：This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission.The primary objective is to develop accurate predictive models for groundwa-ter level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms.Three widely-used machine learning models,namely DT,SVM and RF,were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province,Iran.The study utilized 151 months of GRACE data spanning from 2002 to 2017,which were correlated with piezometer well data available in the study area.The JPL 2 model was selected based on its strong correlation(R=0.9368)with the observed data.The machine learn-ing models were trained and validated using a 70/30 split of the data,and their performance was evaluated 2 using various statistical metrics,including RMSE,R and NSE.The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data.The DT 2 model exhibited the best performance during the calibration stage,with an R value of 0.95,RMSE of 20.655,and NSE of 0.96.The SVM and RF models achieved R values of 0.79 and 0.65,and NSE values of 0.86 and 0.71,respectively.For the prediction stage,the DT model maintained its high efficiency,with an 2 RMSE of 1.48,R of 0.87,and NSE of 0.90,indicating its robustness in predicting future groundwater fluc-tuations using GRACE data.The study highlights the potential of machine learning techniques,particularly Decision Trees,in conjunction with GRACE satellite data,for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions.The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics,provid-ing reliable predictio

关 键 词：Decision Trees Support Vector Machines Random Forests GRACE Satellite Groundwater level 

分 类 号：P641.7[天文地球—地质矿产勘探]