人工智能算法和机器学习的新进化组合用于伊朗西部的山体滑坡易感性测绘
Yue Shen1, Atefeh Ahmadi Dehrashid2,3, Ramin Atash Bahar4
1Tianjin Urban Planning and Design Institute Co., LTD, Tianjin, 300000, China.
Environmental science and pollution research international
|November 21, 2023
概括
机器学习和神经网络显著提高了山体滑坡易感性绘图的准确性. 简单的SVM和Kernel Sigmoid算法获得了完美的分数,有效地识别了高风险区域,以更好地管理山体滑坡危险.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 准确地绘制山体滑坡易感性的地图对于有效的风险管理和城市规划至关重要.
- 优化和混合方法越来越多地用于提高这些地图的精度.
- 新的地形学指数与先进的算法相结合,为改进的山体滑坡检测提供了潜在的潜力.
研究的目的:
- 为了比较新型进化方法的准确性,用于绘制山体滑坡易感性的地图.
- 整合机器学习 (ML) 和人工神经网络 (ANN) 技术与地形学指数.
- 为容易发生山体滑坡的地区开发和验证山体滑坡易感指数 (LSI).
主要方法:
- 研究区域是阿塞拜疆西部,伊朗,一个经常发生山体滑坡的地区.
- 对于160个滑坡事件,分析了16个地质,环境和地形学因素.
- 采用了四个支持矢量机 (SVM) 算法和人工神经网络 (ANN) -MLP,并使用30:70列车测试分割进行了评估.
主要成果:
- 机器学习算法表现出高精度,简单的SVM和Kernel Sigmoid实现了完美的性能得分 (AUC=1).
- 地质参数,斜率,海拔和降雨量被确定为影响滑坡发生的最重要因素.
- 超过80%的研究区域被发现很容易发生山体滑坡,这突显了开发模型的有效性.
结论:
- 机器学习算法,特别是SVM变体,对于土地滑坡易感性评估非常有效.
- 将ML/ANN与地形学指数集成为准确地绘制山体滑坡危险地图提供了一个强大的方法.
- 这些发现强调了地质因素,斜率,海拔和降雨在滑坡预测中的重要性,这对于区域风险减缓工作至关重要.
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