在城市增长的时空效应下预测地下水中的酸盐和硫酸盐动态,使用注意力优化模型
Dinesh Kumar Selvarangam1, S Jayalakshmi2, S S Ramakrishnan3
1Research Scholar, Institute of Remote Sensing, Anna University, Chennai, India. 17131191167@student.annauniv.edu.
Scientific reports
|November 13, 2025
概括
城市扩张显著增加地下水中酸盐和硫酸盐的含量,造成健康风险. 这项研究使用卫星数据和人工智能预测了污染,强调了可持续城市规划的必要性.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 城市规划 城市规划
背景情况:
- 城市化加剧了地下水污染风险的扩散和污水.
- 像反透 (RO) 系统这样的家庭净水器无法去除酸盐和硫酸盐.
- 饮用水中酸盐和硫酸盐含量升高会对人类健康造成危害.
研究的目的:
- 通过整合城市扩张指标和微气候条件来预测地下水中的酸盐和硫酸盐水平.
- 开发和验证混合人工智能模型,用于准确地评估地下水质量.
主要方法:
- 利用哨兵卫星图像用于光谱带特征提取.
- 开发了一个混合模型,结合了基于注意力的卷积神经网络 (ACNN) 和贝叶斯优化多重线性回归 (BO-MLR).
- 应用FP-Growth算法来识别城市扩张指标和污染物度之间的关联.
主要成果:
- 在预测酸盐和硫酸盐水平方面,BO-MLR模型实现了95%的准确性.
- 建筑面积增加50%与酸盐含量增加75%和硫酸盐含量增加60%有关.
- 确定了城市扩张指标和地下水污染物度之间的强烈关联.
结论:
- 随着城市扩张的增加,地下水中酸盐和硫酸盐的含量显著上升.
- 调查结果强调,迫切需要可持续的城市规划和地下水管理.
- 该研究提供了关键的意识,并绘制了加尔帕图地区的危险区域.
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