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在加尔瓦尔喜马拉雅山脉的查莫利地区,基于深度学习的空间合奏方法和任何细分模型来评估山体滑坡风险

Sayantan Mandal¹, Ashis Kumar Saha²

¹Department of Geography, Delhi School of Economics, University of Delhi, New Delhi, 110007, India.

Scientific reports

|December 11, 2025

概括

这项研究引入了一种新的空间整体方法来评估山体滑坡风险 (LRA),将深度学习和自动检测结合起来,以确定高风险区域和建筑脆弱性.

科学领域:

地质科学地质科学
人工智能的人工智能
环境风险评估环境风险评估

背景情况:

土地滑坡风险评估 (LRA) 对于减轻自然灾害至关重要.
现有的方法往往在识别特定的高风险区域和相关的脆弱性方面缺乏准确性.
集成先进的计算技术可以提高LRA的准确性和效率.

研究的目的:

为土地滑坡风险评估 (LRA) 引入一种新的空间整体方法.
在空间集合框架内比较DenseNet神经网络,MLPNN和XGBoost算法的有效性.
制定准确的山体滑坡风险区,并评估靠近山体滑坡的建筑脆弱性.

主要方法:

开发一种空间整体方法,生成两种类型的山体滑坡阻抗复合图.
使用DenseNet神经网络,MLPNN和XGBoost进行山体滑坡预测.
细分任何模型 (SAM) 的应用,用于在潜在的山体滑坡区域附近自动检测建筑物.
使用破裂建筑物和断层线数据进行现场验证和验证.

主要成果:

第二个山体滑坡阻抗复合地图成功确定了特定的高风险位置.
现场验证导致在人类定居点附近发现了新的山体滑坡.
分段任何模型 (SAM) 提供了准确和节省时间的建筑检测.
一个经过验证的山体滑坡风险区有效地划分了建筑物的脆弱性.

结论:

空间集体方法为LRA提供了一种新的,分阶段的方法.
将像DenseNet这样的先进算法与SAM这样的自动检测工具相结合,可以提高LRA的准确性和效率.
这种综合方法为城市规划和减少灾害风险提供了关键的见解.

关键词:

深度学习是一种深度学习.乔希马斯的乔希马斯是什么意思土地滑坡阻力映射地图.分段任何模型 (SAM)空间组合是一个空间组合.

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