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Related Experiment Videos

An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term

Ahmed M Elshewey1, Amel Ali Alhussan2, Doaa Sami Khafaga2

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt.

Plos One
|June 2, 2025
PubMed
Summary

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This summary is machine-generated.

Accurate rainfall prediction is vital for climate studies and agriculture. This study introduces an Adaptive Dynamic Particle Swarm Optimization enhanced with Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for improved rainfall forecasting, achieving a high R2 score.

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Rainfall prediction is critical for climate studies, agriculture, and water management.
  • Accurate analysis of rainfall intensity, duration, and distribution is essential for informed decision-making.
  • Machine learning and metaheuristic algorithms offer advanced solutions for complex prediction challenges.

Purpose of the Study:

  • To introduce a novel hybrid optimization algorithm, Adaptive Dynamic Particle Swarm Optimization enhanced with Guided Whale Optimization Algorithm (AD-PSO-Guided WOA), for rainfall prediction.
  • To enhance feature selection and hyperparameter tuning for rainfall prediction models.
  • To evaluate the performance of the proposed method against conventional techniques.

Main Methods:

Related Experiment Videos

  • A hybrid optimization algorithm (AD-PSO-Guided WOA) was developed to balance global search and local exploitation, overcoming premature convergence.
  • The binary version of AD-PSO-Guided WOA was employed for feature selection from rainfall datasets.
  • Five machine learning models (DT, RF, MLP, LSTM, KNN) were trained using selected features, with LSTM demonstrating superior performance.
  • Hyperparameters of the LSTM model were optimized using the AD-PSO-Guided WOA algorithm.

    • Main Results:

    • The AD-PSO-Guided WOA effectively addressed the premature convergence issue common in optimization algorithms.
    • The Long Short-Term Memory (LSTM) model, optimized with AD-PSO-Guided WOA, achieved the highest prediction accuracy.
    • The proposed AD-PSO-Guided WOA-LSTM methodology demonstrated superior performance with a coefficient of determination (R2) of 0.9636.

    Conclusions:

    • The AD-PSO-Guided WOA-LSTM hybrid approach significantly enhances rainfall prediction accuracy.
    • This novel method offers a robust solution for complex meteorological forecasting tasks.
    • The findings highlight the potential of advanced optimization techniques in improving climate and weather prediction models.