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Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization

Bo Ni1, Li Li1, Hanjie Lin1

  • 1Department of Civil Engineering, Chongqing Three Gorges University, Wanzhou, 404100, Chongqing, China.

Plos One
|April 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved whale optimization algorithm (WOA) to enhance back propagation neural network (BPNN) predictions for debris flow volume. The optimized BPNN demonstrates superior accuracy and stability, even with limited data, identifying landslide sediments as a key factor.

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Area of Science:

  • Geosciences and Environmental Science
  • Computational Science and Machine Learning

Background:

  • Debris flows pose significant threats to mountainous regions, necessitating accurate volume prediction for disaster mitigation.
  • Traditional back propagation neural networks (BPNNs) exhibit instability and inaccuracy with limited datasets for debris flow prediction.

Purpose of the Study:

  • To develop a more accurate and stable debris flow volume prediction model using an optimized back propagation neural network.
  • To investigate the key factors influencing debris flow volume in earthquake-affected areas.

Main Methods:

  • An improved whale optimization algorithm (WOA), incorporating Cubic map and adaptive weight adjustment, was used to optimize BPNN weights and thresholds.
  • Sixty debris flow gullies in the Longmenshan area were analyzed to identify influencing factors.
  • The optimized BPNN model was trained and validated against support vector machine regression, XGBoost, and BPNNs optimized by artificial bee colony and grey wolf algorithms.

Main Results:

  • Loose sediments from co-seismic landslides were identified as the primary factor affecting debris flow volume.
  • The optimized BPNN achieved a mean absolute percentage error of 0.193, mean absolute error of 29.197 × 10^4 m³, and R² of 0.912.
  • The model demonstrated enhanced accuracy and stability, particularly with insufficient and complex datasets.

Conclusions:

  • The Cubic map and adaptive weight adjustment-optimized WOA significantly improves BPNN performance for debris flow volume prediction.
  • The developed model offers a reliable tool for debris flow risk assessment and prevention strategies in mountainous and earthquake-prone regions.
  • This research provides valuable insights into applying advanced machine learning techniques for natural disaster prediction.