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Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor

Praveen Kumar1, Priyanka Priyanka2, K V Uday3

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Summary
This summary is machine-generated.

A new hierarchical transformer prediction autoencoder (H-TPA) model improves landslide hazard prediction in the Himalayas by analyzing weather and soil data with high temporal resolution, crucial for disaster preparedness.

Keywords:
Environmental factorsHierarchical transformerLandslideMachine learningMonitoringVariable sensitivity analysis

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Area of Science:

  • Geosciences
  • Artificial Intelligence
  • Environmental Science

Background:

  • Landslide prediction in the Himalayas is challenging due to complex environmental factors and the need for real-time hazard assessment.
  • Existing Machine Learning (ML) models often lack the temporal resolution and generalization capabilities required for accurate, timely landslide predictions.
  • Current models struggle to incorporate fine-grained weather data (day, hour, minute scales) and provide multi-step ahead predictions essential for disaster management.

Purpose of the Study:

  • To introduce a novel hierarchical ML model, the hierarchical transformer prediction autoencoder (H-TPA), for enhanced slope movement prediction.
  • To improve the accuracy and temporal resolution of landslide hazard prediction, particularly in the vulnerable Himalayan region.
  • To develop a methodology for identifying environmental thresholds that trigger slope movements and analyze the influence of various weather factors.

Main Methods:

  • Utilized a large dataset of 1,066,009 samples (balanced to 23,328 for training) from 64 landslide locations over five years.
  • Developed and implemented the hierarchical transformer prediction autoencoder (H-TPA) model for high-resolution temporal predictions.
  • Employed a VSA methodology to determine critical environmental attribute thresholds and analyzed weather variables (temperature, humidity, pressure, rainfall, sunlight) and soil moisture.

Main Results:

  • The H-TPA model achieved high performance with F1 scores of 0.889 (training), 0.760 (validation), and 0.746 (test) for 10-minute ahead slope movement predictions.
  • Demonstrated enhanced generalization capabilities and high temporal resolution in predicting slope movements.
  • Identified specific threshold values for environmental factors like temperature, humidity, and rainfall that influence landslide triggers.

Conclusions:

  • The H-TPA model offers a significant advancement in landslide prediction accuracy and temporal resolution for the Himalayan region.
  • The study highlights the critical role of fine-scale weather conditions and soil moisture in triggering slope movements.
  • Findings provide valuable insights for improving geo-scientific knowledge, enhancing disaster preparedness, and developing effective mitigation strategies against landslides.