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Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm

Qiying Yu1,2, Chengshuai Liu1, Runxi Li3

  • 1School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, 450001, China.

Scientific Reports
|February 26, 2025
PubMed
Summary

A new flood forecasting model combining particle swarm optimisation (PSO), temporal convolutional network (TCN), and Bootstrap sampling improves accuracy in the Tailan River Basin. While robust, its performance decreases with lead times exceeding 5 hours.

Keywords:
Bootstrap probability sampling algorithmFlood forecastingMachine learningPSO–TCN modelParticle swarm optimization algorithmTailan River BasinTemporal convolutional neural network

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Area of Science:

  • Hydrology
  • Water Resource Management
  • Environmental Engineering

Background:

  • Accurate flood forecasting is essential for effective watershed management and disaster prevention.
  • The Tailan River Basin faces challenges requiring reliable flood prediction systems.

Purpose of the Study:

  • To develop and evaluate an advanced flood forecasting model for the Tailan River Basin.
  • To enhance the accuracy and robustness of hydrological predictions using integrated machine learning and optimization techniques.

Main Methods:

  • Development of the Particle Swarm Optimisation-Temporal Convolutional Network-Bootstrap (PSO-TCN-Bootstrap) flood forecasting model.
  • Utilisation of historical rainfall-runoff data from 50 flood events (1960-2014) for model evaluation.
  • Comparative analysis of model performance under various lead time conditions.

Main Results:

  • The PSO-TCN-Bootstrap model demonstrated superior performance compared to existing methods, indicated by a higher Nash efficiency coefficient.
  • The model achieved lower root mean square and relative peak errors in flood forecasting under consistent lead times.
  • A relative peak error exceeding 20% was observed when the forecasting lead time surpassed 5 hours.

Conclusions:

  • The PSO-TCN-Bootstrap model shows significant applicability and robustness for flood forecasting in the Tailan River Basin.
  • Further research is needed to improve model generalisability and performance for longer lead times.
  • The study provides a scientific basis for enhancing flood management strategies in the region.