基于人工智能的预测地下水腐蚀和半干旱地区的缩放指数,使用25年数据分析
Ali Gorjizade1, Abbas Parsaie2,3
1Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Scientific reports
|November 25, 2025
概括
预测地下水的腐蚀性和缩放性对于基础设施管理至关重要. 像SVM,MARS和ANN这样的机器学习模型准确地评估了水质指数,MARS和ANN显示出卓越的性能.
科学领域:
- 环境科学 环境科学
- 管理水资源 管理水资源
- 机器学习应用 机器学习应用
背景情况:
- 水的腐蚀性和缩对基础设施构成重大威胁,导致成本增加和公共卫生风险增加.
- 可持续的水资源管理需要精确评估水的腐蚀性和缩行为.
- 地下水质量评估对于防止基础设施损坏和确保公共安全至关重要.
研究的目的:
- 用各种机器学习算法建模和预测地下水的腐蚀和缩放行为.
- 评估人工神经网络 (ANN),支持向量机器 (SVM),多变量自适应回归支柱 (MARS) 和随机森林 (RF) 在预测水腐蚀性指数方面的性能.
- 确定最有效的机器学习模型来评估Dezful-Andimeshk平原的地下水质量.
主要方法:
- 采用了四种机器学习算法:ANN,SVM,MARS和RF.
- 利用兰吉尔和指数 (LSI),瑞斯纳稳定指数 (RSI) 和普科里乌斯缩放指数 (PSI) 来评估水的行为.
- 开发了使用25年每日地下水数据的模型,包括吸附比率 (SAR),pH和总溶解固体 (TDS) 作为输入变量.
主要成果:
- 所有测试的算法在预测水的腐蚀性和缩放性方面都表现出可接受的准确性 (R2 = 0.80-0.93).
- SVM模型表现出强的表现,LSI的R2值为0.92,RSI为0.81和PSI为0.82.
- 火星和ANN模型表现出优越和稳定的性能,有效地捕捉数据中的复杂关系.
结论:
- 机器学习模型,特别是MARS和ANN,对于预测地下水腐蚀性和缩放性非常有效.
- 准确预测水质指数有助于可持续的水资源管理和基础设施保护.
- 该研究强调了先进的计算方法在解决与水有关的环境挑战方面的潜力.
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