综合分析层次过程和神经网络方法用于评估曼吉拉河子流域的土壤侵蚀风险,印度
Sachin Kumar1, Mahendra Kumar Choudhary2, Thomas Thomas3
1Civil Engineering Department, MANIT Bhopal, Bhopal, India. sachinkumarphd003@gmail.com.
Scientific reports
|January 23, 2026
概括
本研究介绍了一种综合的分析层次过程 (AHP) -人工神经网络 (ANN) 框架,用于在半干旱地区绘制土壤侵蚀易受性的地图. 这种创新方法提高了可持续土地管理和保护规划的准确性.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 可持续农业 可持续农业
背景情况:
- 土壤侵蚀对半干旱地区的农业可持续性和粮食安全构成重大威胁.
- 有效的土地管理需要创新的侵蚀风险评估框架.
研究的目的:
- 开发和验证一个综合的AHP-ANN框架,用于加强土壤侵蚀易受性测绘.
- 为了确定有针对性的可持续土地管理干预措施的高侵蚀风险地区.
主要方法:
- 利用了来自SRTM DEM,Sentinel-2,Landsat-8和气象数据的十个环境指标.
- 使用分析层次过程 (AHP) 进行因子权重和人工神经网络 (ANN) 进行敏感性分类.
- 集成的AHP分类作为多层感知神经网络的训练数据.
主要成果:
- 综合的AHP-ANN框架实现了86.3%的整体准确率和0.88.8的F1得分.
- 确定曼吉拉河子流域的41%具有高至非常高的侵蚀易感性.
- 改进了分类精度,减少了中度易感性区域,并提供了最终的风险名称.
结论:
- AHP-ANN框架为可持续的土壤保护规划提供了一个强大的,数据驱动的方法.
- 将专家知识的解释性与先进的计算能力相结合,用于改进土地管理决策.
- 提供了在半干旱农业环境中实现可持续发展目标的实际应用.
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