可解释和基于物理的机器学习用于季节性预测季节性水质在孟加拉国帕德玛河流域的季节性水质预测
Abu Reza Md Towfiqul Islam1,2,3, Md Abdullah-Al Mamun4, Md Nashir Uddin5
1Department of Disaster Science and Management, Begum Rokeya University, Rangpur, 5400, Bangladesh. towfiq_dm@brur.ac.bd.
Environmental geochemistry and health
|February 5, 2026
概括
本研究介绍了一种可解释的人工智能 (XAI) 框架,用于预测河水质量,确定酸盐是关键污染物. 该方法提高了数据有限的季风盆地的水质评估.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文地质学 水文地质学
背景情况:
- 亚热带盆地的河水质量显示出由于气候和人类影响而出现的季节性变化.
- 现有的水质指数 (WQI) 模型通常需要大量的数据,缺乏可解释性,阻碍了快速评估.
研究的目的:
- 开发一个集成的可解释的人工智能 (XAI) 框架,用于预测,解释和绘制季节性水质动态.
- 为了应对数据有限的季风驱动河流盆地的水质评估方面的挑战.
主要方法:
- 结合机器学习 (ML),深度学习 (DL) 和物理信息神经网络 (PINNs) 进行WQI预测和解释.
- 使用的地表水样本,WQI评估,可解释的建模,不确定性分析和空间区域化.
- 采用蒙特卡洛模拟来量化不确定性和空间自相关性来识别热点.
主要成果:
- 季节性变化对水质的影响比空间变化更大.
- 酸盐 (NO3-) 被确定为主要污染物,在两个季节都有高水平.
- 不同的ML模型根据季节显示出不同的性能,深度神经网络在冬季和季风中表现出色.
结论:
- 开发的XAI框架提供了可靠,可解释和空间明确的水质预测.
- 该方法改善了季风影响地区适应性水资源管理的决策.
- 基于物理的人工智能增强了模型的概括性和水化学一致性,尤其是在有限的数据的情况下.
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