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This study offers a comprehensive examination of a real-time monitoring framework for a localized landslide early warning system (LEWS), concentrating on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Due to the steep terrain and the slope’s proximity to the highway, continuous monitoring and stability evaluations are crucial. The framework comprises four main phases: monitoring, modeling, forecasting, and warning. Continuous data collection on hydrological and meteorological conditions supported the modeling and calibration efforts. Taylor diagrams were used for validation, ensuring the reliability of predicted data by comparing it with observed values. The calibrated hydrological model was then employed for slope stability analysis to identify factors leading to instability. A machine learning algorithm was implemented to detect potential instabilities. Forecasting predicted periods of instability, triggering alerts when the safety factor ( \(F_{{\text{s}}}\) ) dropped below 1.5. The study highlighted the crucial role of vegetation parameters in improving the accuracy of slope stability assessments using \(F_{{\text{s}}}\) . Calibration of the hydrogeological model, particularly with respect to rainfall, climate, and vegetation, enhanced the match between predicted and observed volumetric water content (VWC), especially at shallower depths. Nevertheless, modeling hydrological dynamics at greater depths remains challenging, necessitating more advanced methods. Machine learning, specifically the Random Forest model, proved highly accurate in predicting \(F_{{\text{s}}}\) , with VWC at depths of 0.3 and 3 m being key variables. Analysis indicated that a 12-month simulation period is most effective, providing consistent results across various depths. This research advances slope stability modeling and supports sustainable slope management practices, highlighting the intricate interactions between climate, vegetation, and hydrological processes in unsaturated slopes. It lays the groundwork for developing effective landslide early warning systems in the Indian Himalayan region.

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Integrated Instrumentation and Hydrogeotechnical Modeling for Landslide Risk Assessment

  • 会议论文

  • 发表日期：

    2025年

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  • Neelima Satyam,
  • Kunal Gupta

摘要

This study offers a comprehensive examination of a real-time monitoring framework for a localized landslide early warning system (LEWS), concentrating on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Due to the steep terrain and the slope’s proximity to the highway, continuous monitoring and stability evaluations are crucial. The framework comprises four main phases: monitoring, modeling, forecasting, and warning. Continuous data collection on hydrological and meteorological conditions supported the modeling and calibration efforts. Taylor diagrams were used for validation, ensuring the reliability of predicted data by comparing it with observed values. The calibrated hydrological model was then employed for slope stability analysis to identify factors leading to instability. A machine learning algorithm was implemented to detect potential instabilities. Forecasting predicted periods of instability, triggering alerts when the safety factor ( \(F_{{\text{s}}}\) ) dropped below 1.5. The study highlighted the crucial role of vegetation parameters in improving the accuracy of slope stability assessments using \(F_{{\text{s}}}\) . Calibration of the hydrogeological model, particularly with respect to rainfall, climate, and vegetation, enhanced the match between predicted and observed volumetric water content (VWC), especially at shallower depths. Nevertheless, modeling hydrological dynamics at greater depths remains challenging, necessitating more advanced methods. Machine learning, specifically the Random Forest model, proved highly accurate in predicting \(F_{{\text{s}}}\) , with VWC at depths of 0.3 and 3 m being key variables. Analysis indicated that a 12-month simulation period is most effective, providing consistent results across various depths. This research advances slope stability modeling and supports sustainable slope management practices, highlighting the intricate interactions between climate, vegetation, and hydrological processes in unsaturated slopes. It lays the groundwork for developing effective landslide early warning systems in the Indian Himalayan region.