- Book学术

发布求助

文献互助智能选刊最新文献

IF 2.8 4区环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES

Environmental Earth Sciences Pub Date : 2025-03-03 DOI:10.1007/s12665-025-12132-4

Ahmed Makhlouf, Mustafa El-Rawy, Shinjiro Kanae, Mahmoud Sharaan, Ali Nada, Mona G. Ibrahim

{"title":"Streamlining the monitoring and assessment of irrigation groundwater quality using machine learning techniques","authors":"Ahmed Makhlouf, Mustafa El-Rawy, Shinjiro Kanae, Mahmoud Sharaan, Ali Nada, Mona G. Ibrahim","doi":"10.1007/s12665-025-12132-4","DOIUrl":null,"url":null,"abstract":"<div><p>Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 5","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12665-025-12132-4.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Earth Sciences","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s12665-025-12132-4","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

持续评估地下水质量对确保其长期可持续性至关重要。然而，由于成本和时间限制，用于各种目的的传统评估方法面临挑战。本研究采用机器学习（ML）模型，包括高斯过程回归（GPR）、决策树（DT）、支持向量回归（SVR）和人工神经网络（ANN），仅使用物理参数（电导率（EC）和 pH 值）和现场条件（海拔高度、地下水位深度和与河流的距离）来预测五个灌溉水质量（IWQ）指数。收集并分析了埃及米尼亚全新统含水层的 246 个地下水样本数据集，以测量地下水水质参数。利用五种输入参数组合来计算综合水质指数：钠吸附率 (SAR)、钠百分比 (Na%)、总硬度 (TH)、渗透指数 (PI) 和凯尔比 (KR)。仅根据物理测量结果和现场条件就建立了 ML 模型来估算 IWQ 参数。结果显示，在训练过程中，GPR、DT、SVR 和 ANN 对所有 IWQ 参数都有很强的预测能力。结果表明，GPR 能准确预测地下水质量，其次是 DT、SVR 和 ANN。GPR 模型的最佳性能是在第四种组合中实现的，其中包括 EC 和到河流的距离。通过第四种组合对 GPR 的评估发现，在预测 SAR、%Na、TH、PI 和 KR 时，相关系数分别为 0.97、0.82、0.96、0.87 和 0.81，准确度最高。该研究强调了机器学习模型有效利用现成和可量化的现场数据来预测 IWQ 特征的能力。此外，这些研究成果有助于实现可持续发展目标（SDGs）的第二个目标 "无饥饿 "和第六个目标 "清洁水和卫生设施"，具有提高农业生产力和水资源保护的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Streamlining the monitoring and assessment of irrigation groundwater quality using machine learning techniques

Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Environmental Earth Sciences 环境科学-地球科学综合

CiteScore

5.10

自引率

3.60%

发文量

494

审稿时长

8.3 months

期刊介绍： Environmental Earth Sciences is an international multidisciplinary journal concerned with all aspects of interaction between humans, natural resources, ecosystems, special climates or unique geographic zones, and the earth: Water and soil contamination caused by waste management and disposal practices Environmental problems associated with transportation by land, air, or water Geological processes that may impact biosystems or humans Man-made or naturally occurring geological or hydrological hazards Environmental problems associated with the recovery of materials from the earth Environmental problems caused by extraction of minerals, coal, and ores, as well as oil and gas, water and alternative energy sources Environmental impacts of exploration and recultivation – Environmental impacts of hazardous materials Management of environmental data and information in data banks and information systems Dissemination of knowledge on techniques, methods, approaches and experiences to improve and remediate the environment In pursuit of these topics, the geoscientific disciplines are invited to contribute their knowledge and experience. Major disciplines include: hydrogeology, hydrochemistry, geochemistry, geophysics, engineering geology, remediation science, natural resources management, environmental climatology and biota, environmental geography, soil science and geomicrobiology.