基于GeoAI的布拉马普特拉河流域土壤侵蚀风险评估:使用RUSLE和先进机器学习的协同方法
Toushif Jaman1, Shashank Bhaskar2, Victor Saikhom1
1North Eastern Space Applications Centre (NESAC), Department of Space, Govt. of India, Shillong, India.
Environmental monitoring and assessment
|July 10, 2025
概括
布拉马普特拉河流域的土壤侵蚀从2005年到2024年显著增加了60.76%,从2005年到2024年. 先进的AI模型突出了迫切需要的土壤保护和流域管理策略.
科学领域:
- 环境科学 环境科学
- 地质科学是地球科学.
- 遥感 遥感 遥感 遥感
背景情况:
- 土壤侵蚀对布拉马普特拉河流域的农业,水资源和生态稳定构成严重威胁.
- 了解侵蚀动态对于有效的环境管理和气候适应至关重要.
研究的目的:
- 从2005年到2024年,分析布拉马普特拉河流域的土壤侵蚀模式.
- 评估地形和植被覆盖对侵蚀率的影响.
- 评估用于土壤侵蚀分析的机器学习模型的预测性能.
主要方法:
- 修订的普世土壤损失方程 (RUSLE) 集成与遥感和GIS.
- 随机森林 (RF) 和梯度提升 (GB) 机器学习模型的应用.
- 对地形 (LS因子),降雨侵蚀性 (R因子) 和植被覆盖 (C因子) 数据的分析.
主要成果:
- 在2005年至2024年期间,平均每年土壤损失增加了60.76%,从15.8/公/年增加到25.4/公/年.
- 在局部热点地区,侵蚀率达到32.130/公/年.
- 尽管植被状况有所改善,但的坡度 (47.2%>16°) 和波动的降雨侵蚀性导致侵蚀风险高.
- 渐变增强模型显示出优异的预测准确性 (R2=0.952,RMSE=3.97).
结论:
- 布拉马普特拉河流域的土壤侵蚀正在升级,需要立即进行保护干预.
- 人工智能驱动的建模与GIS和遥感相结合,为长期侵蚀监测提供了一个强大的工具.
- 调查结果支持为可持续的流域管理和气候适应战略做出明智决策.
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