使用机器学习和形态分析对尼亚尔盆地地下水潜在区域进行空间建模
R J Jerin Joe1,2, V Stephen Pitchaimani3, S Kaliraj4
1PG and Research Department of Geology, V.O. Chidambaram College, Thoothukudi, Tamil Nadu, 628008, India. jerinjoe621@gmail.com.
Scientific reports
|October 16, 2025
概括
这项研究利用形态分析和机器学习在尼亚尔盆地绘制了地下水潜在区域的地图. 在山谷和破裂岩石中发现了高潜力区域,有助于可持续的水资源管理.
科学领域:
- 地球科学 地球科学 地球科学
- 水文学 水文学
- 地理信息学是一种地理信息学.
背景情况:
- 地下水是重要的资源,需要准确地绘制地图,以实现可持续管理.
- 传统的地下水潜在区域测绘方法往往缺乏精度.
- 形态分析和机器学习为水文评估提供了先进的工具.
研究的目的:
- 在尼亚尔盆地划定地下水潜在区域 (GWPZs).
- 评估60个形态参数对地下水发生的影响.
- 整合地理空间数据和机器学习,以提高地下水绘图的准确性.
主要方法:
- 使用SRTM DEM (30m分辨率) 进行了形态分析,以提取线性,面积,浮雕和排水纹理参数.
- 集成的主题层 (地质,土地利用,土壤,坡度,降雨量) 和井数据来训练随机森林机器学习模型.
- 根据模型输出,将Neyyar盆地分为高,中和低地下水潜在区域.
主要成果:
- 在山谷,低坡和破碎岩石地区确定了高地下水潜在区域 (43.27%).
- 位于地带的低潜力区域 (34.96%),排水密度高,透度低.
- 确认的排水密度,分叉比率和浮地特征作为地下水发生的关键指标.
结论:
- 机器学习与形态分析的整合显著提高了地下水潜力绘图的准确性.
- 该研究为数据稀缺地区的可持续地下水资源管理提供了强有力的框架.
- 了解形态参数的相互作用对于预测地下水的可用性至关重要.
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