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Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts.

Ali Danandeh Mehr1, Mir Jafar Sadegh Safari2

  • 1Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey. ali.danandeh@antalya.edu.tr.

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Summary

A new hybrid machine learning model, multiple genetic programming (MGP), enhances 1-month ahead rainfall forecasting accuracy. MGP outperforms standard methods, offering significant error reduction for arid regions.

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Genetic programmingHybrid modelsRainfallStochastic modelling

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Area of Science:

  • Environmental Science
  • Computer Science
  • Meteorology

Background:

  • Standalone machine learning models struggle with long-lead rainfall forecasting, especially in arid regions.
  • Accurate rainfall prediction is crucial for water resource management in vulnerable areas.

Purpose of the Study:

  • To introduce and evaluate a novel hybrid machine learning model, multiple genetic programming (MGP), for improved 1-month ahead rainfall forecasting.
  • To assess the MGP model's performance against established benchmark models in arid conditions.

Main Methods:

  • Developed a hybrid model (MGP) combining multigene genetic programming with a classic genetic programming engine.
  • Employed a multi-step evolutionary search algorithm for gene recombination.
  • Validated the MGP model using rainfall data from two stations in Iran's Lake Urmia Basin.

Main Results:

  • The MGP model demonstrated statistically superior performance compared to standard genetic programming (GP) and autoregressive state-space models.
  • MGP achieved reductions in absolute errors by up to 15% and relative errors by up to 40%.
  • The model's explicit structure facilitates practical application.

Conclusions:

  • The MGP model offers a significant advancement over standalone GP for 1-month ahead rainfall forecasting.
  • MGP provides a robust and accurate solution for rainfall prediction challenges in arid and semiarid regions.
  • The model's improved accuracy and interpretability support its adoption in operational forecasting systems.