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在Shabestar平原使用GALDIT和AI技术对海水入侵脆弱性的时间评估

Vahid Nourani^1,2, Elnaz Bayat Khajeh¹, Nardin Jabbarian Paknezhad¹

¹Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, 5166616471, Iran.

Environmental science and pollution research international

|April 2, 2025

概括

在沿海地区,地下水对海水入侵 (SWI) 的脆弱性正在增加. 这项研究使用了GALDIT方法和AI模型来评估含水层的脆弱性,揭示了情况的恶化,并确定了改善管理的关键影响因素.

科学领域:

环境科学环境科学
水文地质学水文地质学
水资源管理水资源管理

背景情况:

海水入侵 (SWI) 是沿海地下水资源的重大威胁,特别是在干旱地区.
有效地管理地下水对于淡水供应至关重要.
盖尔迪特方法是一种基于指数的方法,用于评估水表对SWI的脆弱性.

研究的目的:

通过使用GALDIT方法对2002年,2012年和2022年的Shabestar水层对SWI的脆弱性进行评估.
通过应用AI模型 (ANNs和RF) 来减轻GALDIT方法中的专家判断的主观性.
为了更好地管理水质,确定影响电导率 (EC) 和总溶解固体 (TDS) 的最有影响力的参数.

主要方法:

采用了GALDIT方法,对六个关键参数 (G,A,L,D,I,T) 进行评分,以便随着时间的推移绘制下水层脆弱性的地图.
使用人工神经网络 (ANN) 和随机森林 (RF) 模型,使用GALDIT参数作为输入来估计EC和TDS.
进行了Sobol灵敏度分析,以确定GALDIT参数对EC和TDS的影响.

主要成果:

水库脆弱性测绘显示,非常高脆弱性地区的增长率从10.9% (2002年) 增加到17.8% (2022年),表明条件恶化.
在估计EC和TDS方面,ANN模型表现优于RF模型,精度提高了10%和9% (DC).
灵敏度分析确定"现有海水入侵的影响" (I) 和"距离海岸线" (D) 对EC最有影响",I"和"含水层厚度" (T) 对TDS最有影响.

结论:

沙贝斯塔水层对SWI的脆弱性在过去二十年中显著增加.
人工智能模型,特别是ANN,提高了脆弱性评估和预测水质参数的准确性.
了解特定GALDIT参数的影响对于针对SWI的有针对性和有效的地下水管理策略至关重要.

关键词:

人工智能的人工智能是人工智能.沿海的水源层是沿海的.盖尔迪特指数 (GALDIT Index) 是一个指数.地下水的脆弱性地下水的脆弱性海水的入侵海水的入侵灵敏度分析是一种灵敏度分析.

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