在巴丁达区使用深度学习进行水地化学评估和地下水化物预测
Kamalpreet Kaur1, Kaikho Khusulio2, Neeta Raj Sharma3
1School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India.
Environmental monitoring and assessment
|March 15, 2026
概括
旁遮普邦巴丁达的地下水化物污染是一个公共卫生风险. 深度学习模型准确地预测化物水平,识别高风险区域,以改善水资源管理和卫生规划.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 公共卫生 公共卫生
背景情况:
- 地下水的化物污染对公众健康构成重大风险,特别是在旁遮普邦巴丁达等半干旱地区.
- 有限的研究存在,包括空间采样,水地化学分析和地下水化物的先进预测建模.
- 严重依赖地下水饮用和日常使用加剧了这种担忧.
研究的目的:
- 使用深度学习技术自动化地下水化物预测.
- 为了评估巴丁达地区的季节性水化变化.
- 识别化物热点及其与环境因素的关联.
主要方法:
- 在季风前和季风季节采集了226个地下水样本,使用基于GIS的采样.
- 分析了水化学参数,并计算了水质指数 (WQI).
- 采用机器学习和深度学习模型 (CNN,LSTM,DNN,CNN-LSTM) 与数据增强用于化物预测.
主要成果:
- 深度神经网络 (DNN) 模型表现出卓越的性能,R2值为0.92 (季风前) 和0.91 (季风).
- 空间分析发现化物热点超过世卫组织限制 (>1.5 ppm).
- 化物热点与特定的石质学,土地使用土地覆盖 (LULC) 和地形学密切相关.
结论:
- 综合方法可以在未采样区域准确预测化物,从而促进早期风险识别.
- 调查结果支持对地下水管理,环境监测和公共卫生规划的知情决策.
- 深度学习模型提供了一个强大的工具来应对地下水化物污染的挑战.
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