Scholarlink

Introduction and Research Gap <p>This study presents a comprehensive framework for predicting volumetric water content (VWC) to mitigate shallow, rainfall-induced landslides, bridging existing gaps in the literature.</p> Methodology <p>The framework synergistically integrates the empirical strengths of deep learning (DL) with the physical dynamics of the VWC subsurface behavior. Statistical, shallow machine learning (ML), and DL models were investigated with optimization techniques and sensitivity analyses to establish benchmarks for comparison and derive optimal predictions. DL and probability theory enable both point and interval predictions.</p> Findings <p>Validation on the Pa Mei landslide demonstrates strong performance with mean absolute errors (MAE) ranging from 0.35% to 1.22% and Predicted Interval Coverage Probabilities (PICP) from 0.86 to 0.91. Predicted VWC deviations were propagated into Factor of Safety (FOS) calculations, yielding robust performance metrics with R<sup>2</sup> and PICP of 0.89 and 0.85, respectively. Transferability is demonstrated at the Tung Chung landslide, where MAE ranges from 0.36% to 1.25% and PICP from 0.86 to 0.95.</p> Significance <p>This framework demonstrates improved accuracy and introduces a practical data-sharing mechanism to address monitoring challenges such as power consumption and data loss, offering a robust tool for hazard mitigation and decision support.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A deep learning-based model for endorsing predictive accuracies of landslide prediction: insights into soil moisture dynamics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月6日

查看全文
  • Kyrillos Ebrahim,
  • Eslam Mohammed Abdelkader,
  • Tarek Zayed,
  • Mohamed A. Meguid

摘要

Introduction and Research Gap

This study presents a comprehensive framework for predicting volumetric water content (VWC) to mitigate shallow, rainfall-induced landslides, bridging existing gaps in the literature.

Methodology

The framework synergistically integrates the empirical strengths of deep learning (DL) with the physical dynamics of the VWC subsurface behavior. Statistical, shallow machine learning (ML), and DL models were investigated with optimization techniques and sensitivity analyses to establish benchmarks for comparison and derive optimal predictions. DL and probability theory enable both point and interval predictions.

Findings

Validation on the Pa Mei landslide demonstrates strong performance with mean absolute errors (MAE) ranging from 0.35% to 1.22% and Predicted Interval Coverage Probabilities (PICP) from 0.86 to 0.91. Predicted VWC deviations were propagated into Factor of Safety (FOS) calculations, yielding robust performance metrics with R2 and PICP of 0.89 and 0.85, respectively. Transferability is demonstrated at the Tung Chung landslide, where MAE ranges from 0.36% to 1.25% and PICP from 0.86 to 0.95.

Significance

This framework demonstrates improved accuracy and introduces a practical data-sharing mechanism to address monitoring challenges such as power consumption and data loss, offering a robust tool for hazard mitigation and decision support.