A machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions with high reliance on groundwater irrigation
Feilin Zhu , Mingyu Han , Yimeng Sun , Yurou Zeng , Lingqi Zhao , Ou Zhu , Tiantian Hou , Ping-an Zhong
{"title":"A machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions with high reliance on groundwater irrigation","authors":"Feilin Zhu , Mingyu Han , Yimeng Sun , Yurou Zeng , Lingqi Zhao , Ou Zhu , Tiantian Hou , Ping-an Zhong","doi":"10.1016/j.envsoft.2024.106146","DOIUrl":null,"url":null,"abstract":"<div><p>This study presents a machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions heavily reliant on groundwater irrigation. The framework utilizes a comprehensive set of predictive factors, including meteorological, hydrological, and human activity data. An optimal combination of input variables and their temporal delays was determined using a novel selection method. To address overfitting, a mathematical model for hyperparameter optimization was developed, leveraging sample subset cross-validation and an improved differential evolution algorithm. Numerical experiments on the YingGuo region in the Huaihe River Basin demonstrated that the hyperparameter optimization resulted in an 11.6%–38.5% increase in the Nash-Sutcliffe Efficiency (NSE) indicator. Additionally, fine-tuned temporal scales, from monthly to five-day resolution, significantly improved predictive performance, with NSE increasing from 0.629 to 0.952 (33.9% enhancement). However, longer forecasting horizons led to a 29.4% reduction in NSE. The study also implemented a multi-core parallel computing framework, which achieved a 15.35-fold improvement in computational efficiency while maintaining predictive precision. The integration of external factors enhanced the predictive performance across various observation wells. These findings contribute to a better understanding of groundwater dynamics and highlight the potential of machine learning models in improving groundwater depth predictions in agricultural regions with high reliance on groundwater irrigation.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"180 ","pages":"Article 106146"},"PeriodicalIF":4.8000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136481522400207X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This study presents a machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions heavily reliant on groundwater irrigation. The framework utilizes a comprehensive set of predictive factors, including meteorological, hydrological, and human activity data. An optimal combination of input variables and their temporal delays was determined using a novel selection method. To address overfitting, a mathematical model for hyperparameter optimization was developed, leveraging sample subset cross-validation and an improved differential evolution algorithm. Numerical experiments on the YingGuo region in the Huaihe River Basin demonstrated that the hyperparameter optimization resulted in an 11.6%–38.5% increase in the Nash-Sutcliffe Efficiency (NSE) indicator. Additionally, fine-tuned temporal scales, from monthly to five-day resolution, significantly improved predictive performance, with NSE increasing from 0.629 to 0.952 (33.9% enhancement). However, longer forecasting horizons led to a 29.4% reduction in NSE. The study also implemented a multi-core parallel computing framework, which achieved a 15.35-fold improvement in computational efficiency while maintaining predictive precision. The integration of external factors enhanced the predictive performance across various observation wells. These findings contribute to a better understanding of groundwater dynamics and highlight the potential of machine learning models in improving groundwater depth predictions in agricultural regions with high reliance on groundwater irrigation.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.