用地统计和整体人工智能工具对地下水质量和数量的时空评估
Vahid Nourani1, Amirreza Ghaffari2, Nazanin Behfar2
1Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; Faculty of Civil and Environmental Engineering, Near East University, Via Mersin 10, Turkey; College of Engineering, Information Technology and Environment, Charles Darwin University, Australia.
Journal of environmental management
|March 3, 2024
概括
这项研究使用人工智能和地球统计学将地表水数据与地下水质量联系起来. 先进的模型准确地预测地下水质量,减少实验室测试并帮助可持续的水资源管理.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 了解地表水文和地下水之间的时空动态对于有效的水资源管理至关重要.
- 评估地下水质量的传统方法往往耗时且资源密集.
- 水库污染和过度开采对水资源构成重大威胁,如马拉盖水库所见.
研究的目的:
- 研究地表水文变量与地下水质量/数量之间的时空关系.
- 开发和评估人工智能 (AI) 模型,以利用遥感和地面数据估计地下水质量.
- 分析影响马拉盖水层地下水质量的因素,并评估预测模型的性能.
主要方法:
- 采用地理统计技术,包括各种Kriging方法,用于空间绘图和数据缺口填补.
- 开发并比较了三个人工智能模型:前神经网络 (FFNNs),支持向量回归 (SVR) 和自适应神经模糊推理系统 (ANFIS).
- 利用组合方法将AI模型输出结合起来,以提高地下水质量指数 (GWQI) 的预测准确度.
主要成果:
- 地理统计模型以0.8508和1.1688.8之间的根平均平方尺度误差 (RMSSE) 值显示了可接受的性能.
- 人工智能模型准确地预测了地下水质量,在潮湿月份达到0.88的确定系数 (R2) 值,在干燥月份达到0.84的确定系数 (R2).
- 组合模型进一步提高了预测准确度,高达6% (雨季) 和12% (干季).
结论:
- 人工智能和地球统计工具为地下水质量和数量的时空分析提供了强大的方法.
- 开发的AI模型有效地估计了地下水质量,减少了对广泛实验室分析的需求.
- 调查结果强调,由于人为因素,马拉盖水层的地下水质量下降,强调需要改进水资源管理策略.
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