地中海地区尼罗河三角洲含水层脆弱性评估的机器学习增强GALDIT模型

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL
Zenhom El-Said Salem , Nesma A. Arafa , Abdelaziz L. Abdeldayem , Youssef M. Youssef
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引用次数: 0

摘要

由于过度开采和全球气候变化不稳定,巨型三角洲含水层面临着日益严重的盐渍化风险。本研究将GALDIT框架与机器学习(ML)模型,即支持向量机(SVM)、广义线性模型(GLM)和极端梯度增强(XGBoost)相结合,以增强三角洲含水层对海水入侵(SWI)的脆弱性(DAV)评估。尼罗河三角洲,最大的淡水巨型三角洲含水层,可以作为一个案例研究。利用GALDIT因子(地下水产状、含水层导电性、地下水海拔高度、距海岸线距离、现有海水入侵影响、含水层厚度)对模型进行网格搜索超参数优化,并以总溶解盐(TDS)为输入变量调整条件脆弱性指数(CVI)。包括均方根误差(RMSE)、平均绝对误差(MAE)、均方误差(MSE)、决定系数(R2)、Pearson相关系数(r)、Nash-Sutcliffe效率(NSE)、观测值与标准差的均方根误差(RSR)和散点指数(IOS)在内的统计指标表明,XGBoost模型显著优于SVM和GLM,并取得了优异的成绩:R2 = 0.9622, RMSE = 0.0430, r = 0.9815,美= 0.0206,MSE = 0.0018,分析了无= 0.9618,IOS秩= 0.0005,= 0.2935。GALDITXGBoost地图确定了亚历山大西部以前未被发现的高脆弱性地区以及地中海沿岸塞得港南部的局部地区。与基本的GALDIT相比,中等脆弱区扩大了,特别是在伊斯梅利亚北部。Piper图证实了SWI的风险,主要的Na-Cl和Ca-Mg-Cl相表明Cl和SO₄2毒血症水平升高。从HCO₃到Cl⁻的转变进一步证实了盐碱化,而Ca-HCO₃相代表淡水。优化后的XGBoost模型为管理大三角洲地下水和评估全球三角洲脆弱性提供了一个强大的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region
Mega-delta aquifers face increasing salinization risks from overexploitation and erratic climate change globally. This study integrates the GALDIT framework with machine learning (ML) models, namely Support Vector Machine (SVM), Generalized Linear Model (GLM), and eXtreme Gradient Boosting (XGBoost), to enhance delta aquifer vulnerability (DAV) assessment to seawater intrusion (SWI). The Nile Delta, the largest freshwater mega-delta aquifer, serves as a case study. Grid search hyperparameter optimization was applied to refine these models using the GALDIT factors (groundwater occurrence, aquifer hydraulic conductivity, groundwater height above sea level, distance from the shoreline, impact of existing seawater intrusion, and aquifer thickness) and adjust conditioned vulnerability index (CVI) based on Total Dissolved Salts (TDS) as input variables. Statistical metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Coefficient of Determination (R2), Pearson Correlation Coefficient (r), Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error to Standard Deviation of Observations (RSR), and Index of Scatter (IOS), show that the XGBoost model significantly outperforms SVM and GLM, with exceptional results: R2 = 0.9622, RMSE = 0.0430, r = 0.9815, MAE = 0.0206, MSE = 0.0018, NSE = 0.9618, RSR = 0.0005, and IOS = 0.2935. The GALDITXGBoost map identified previously undetected high-vulnerability areas west of Alexandria and localized pockets within southern Port Said along the Mediterranean coast. The moderate vulnerability zone expanded, especially in northern Ismailia, compared to the basic GALDIT. Piper diagrams confirmed SWI risks, with dominant Na-Cl and Ca-Mg-Cl facies indicating elevated Cl⁻ and SO₄2⁻ levels. A shift from HCO₃⁻ to Cl⁻ further validated salinization, while Ca-HCO₃ facies represented freshwater. The optimized XGBoost model offers a robust tool for managing mega-delta groundwater and assessing global delta vulnerabilities.
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来源期刊
Groundwater for Sustainable Development
Groundwater for Sustainable Development Social Sciences-Geography, Planning and Development
CiteScore
11.50
自引率
10.20%
发文量
152
期刊介绍: Groundwater for Sustainable Development is directed to different stakeholders and professionals, including government and non-governmental organizations, international funding agencies, universities, public water institutions, public health and other public/private sector professionals, and other relevant institutions. It is aimed at professionals, academics and students in the fields of disciplines such as: groundwater and its connection to surface hydrology and environment, soil sciences, engineering, ecology, microbiology, atmospheric sciences, analytical chemistry, hydro-engineering, water technology, environmental ethics, economics, public health, policy, as well as social sciences, legal disciplines, or any other area connected with water issues. The objectives of this journal are to facilitate: • The improvement of effective and sustainable management of water resources across the globe. • The improvement of human access to groundwater resources in adequate quantity and good quality. • The meeting of the increasing demand for drinking and irrigation water needed for food security to contribute to a social and economically sound human development. • The creation of a global inter- and multidisciplinary platform and forum to improve our understanding of groundwater resources and to advocate their effective and sustainable management and protection against contamination. • Interdisciplinary information exchange and to stimulate scientific research in the fields of groundwater related sciences and social and health sciences required to achieve the United Nations Millennium Development Goals for sustainable development.
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