{"title":"高度依赖地下水灌溉的农业地区地下水位多步提前预测的机器学习框架","authors":"","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":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions with high reliance on groundwater irrigation\",\"authors\":\"\",\"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\":null,\"pages\":null},\"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}","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}
A machine learning framework for multi-step-ahead prediction of groundwater levels in agricultural regions with high reliance on groundwater irrigation
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.