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Updated: Jun 28, 2025

An R-Based Landscape Validation of a Competing Risk Model
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滑坡易感性映射的游戏理论优化:贝叶斯优化的基本神经网络和新一代神经网络模型之间的比较研究.

Javed Mallick1, Meshel Alkahtani2, Hoang Thi Hang3

  • 1Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia. jmallick@kku.edu.sa.

Environmental science and pollution research international
|April 9, 2024
PubMed
概括

这项研究优化了深度神经网络 (DNN),Elman神经网络 (ENN) 和人工神经网络 (ANN) 以绘制山体滑坡易感性的地图. 深度神经网络模型在识别高风险山体滑坡区域方面表现出卓越的性能.

关键词:
贝叶斯优化的贝叶斯优化减轻滑坡风险 减轻滑坡风险容易发生山体滑坡的易感性神经网络的神经网络沙普利的添加式扩展 (SHAP)

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