使用结合的人工智能模型进行地下水化风险评估
Oussama Dhaoui1,2, Isabel Margarida Antunes3, Ines Benhenda4
1Higher Institute of Water Sciences and Techniques, Applied Hydrosciences Laboratory, University of Gabes, University Campus, 6033, Gabes, Tunisia. dhaoui.oussama2013@gmail.com.
Environmental science and pollution research international
|April 28, 2024
概括
人工智能模型,包括人工神经网络 (ANN),改善了干旱地区地下水化风险评估. 在预测地下水污染的传统方法中,ANN模型显示出更高的准确性.
科学领域:
- 水文地质学 水文地质学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 地下水污染风险评估对于水资源管理至关重要,特别是在干旱地区,如突尼斯的Menzel Habib.
- 最初的DRASTIC脆弱性方法在准确反映地下水化风险方面存在局限性.
- 现有的方法显示,极大脆弱性指数 (VI) 和地下水总溶解固体 (TDS) 之间的相关性很弱.
研究的目的:
- 开发和验证人工智能 (AI) 模型,用于评估地下水盐化风险 (GSR).
- 通过结合TDS-GSR指标来增强原来的DRASTIC方法.
- 为了比较ANN,SVR和MLR模型在预测GSR方面的性能.
主要方法:
- 人工神经网络 (ANN),支持向量回归 (SVR) 和多重线性回归 (MLR) 算法的应用.
- 使用原始DRASTIC模型的七个参数作为输入.
- 使用来自TDS的地下水盐化风险 (GSR) 值作为AI模型的输出.
主要成果:
- 原始的DRASTIC VI表明脆弱性低至中等 (91-141),但与TDS的相关性很弱 (r <0.5).
- 在培训期间,ANN模型表现出卓越的性能,实现0.89的相关系数 (r) 和0.4的威尔莫特协议指数 (d).
- 基于ANN的方法证明比SVR,MLR和原来的DRASTIC模型更强大,更准确.
结论:
- 人工智能模型,特别是ANN,为评估地下水化风险提供了更准确和更强大的方法.
- 与原来的DRASTIC模型相比,纳入TDS-GSR指标显著改善了地下水脆弱性评估.
- 这些发现为干旱环境中管理地下水污染风险提供了宝贵的见解.
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