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整合地理空间技术和人工神经网络,用于印度的地下水潜在区分
Pradeep Kumar Badapalli1, Anusha Boya Nakkala2, Padma Sree Pujari3
1CSIR-National Geophysical Research Institute, Uppal Road, Hyderabad, Telangana, 500007, India.
Scientific reports
|January 19, 2026
概括
在Mahi河流域,地下水压力正在上升. 本研究使用遥感和人工神经网络 (ANN) 模型绘制了地下水潜在区域的地图,以指导可持续管理.
科学领域:
- 水文学的水文学
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 城市化和农业的增长加剧了马希河流域的地下水压力.
- 局限的自然充电加剧了该地区地下水的枯竭.
研究的目的:
- 在马希河流域划定地下水潜在区域 (GWPZs).
- 开发一个强大的数据驱动型号,用于可持续的地下水资源管理.
主要方法:
- 利用十个预测因素 (地质,地形,斜率,密度,植被指数,水指数,土壤指数) 来自多源卫星图像和现场数据.
- 采用人工神经网络 (ANN) 模型来分配非线性权重并生成GWPZ地图.
- 使用ROC曲线下的面积 (AUC=0.850) 和井位置数据验证模型性能.
主要成果:
- 创建了一个GWPZ地图,其中有五个类别:非常好 (5.24%),好 (16.35%),中等 (29.89%),穷 (30.88%),非常穷 (17.64%).
- 超过62%的盆地属于中等到非常差的地下水潜在区域.
- 克鲁斯卡尔-瓦利斯H测试证实了预测类之间的显著水文地质差异.
结论:
- 通过将ANN与遥感和GIS集成,为地下水管理提供了一个强大的工具.
- 战略地下水充电规划至关重要,因为这些地区面积很大,潜力中等到非常低.
- 开发的模型适用于数据稀缺,半干旱地区的可持续地下水管理.
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