Monthly rainfall forecasting modelling based on advanced machine learning methods: tropical region as case study

Mohammed Falah Allawi, Uday Hatem Abdulhameed, Ammar Adham, Khamis Naba Sayl, Sadeq Oleiwi Sulaiman, Majeed Mattar Ramal, Mohsen Sherif, Ahmed El-Shafie

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Existing forecasting methods employed for rainfall forecasting encounter many limitations, because the difficulty of the underlying mathematical proceeding in dealing with the patterning and imitation of rainfall data. This study attempts to provide a robust methodology for detecting the nonlinearity of the rainfall pattern by integrating several optimizer algorithms with an Artificial Neural Network (ANN). The Artificial Bee Colony, Particle Swarm Optimization, and Imperialism Competitive Algorithm have been integrated to improve and optimize the internal parameters of the ANN method. In Malaysia, a real-world case study was set up, and the ANN model was created using 54 years (1967–2020) worth of local monthly data. The created artificial neural network method is being utilized for rainfall forecasting in real-time. A variety of network types were evaluated with various input information types with the goal of producing accurate rainfall forecasts. Statistical analysis was conducted using various statistical indicators to evaluate the model’s accuracy in forecasting rainfall. The study revealed that the model based on the integration of the Imperial Competitive Algorithm with Artificial Neural Network (ICA-ANN) outperformed other predictive models. The results confirmed that the proposed model (ICA-ANN) is a promising predictive model for forecasting monthly rainfall with high accuracy.

Original languageEnglish
Article number2243090
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 2023


  • AI-models
  • Rainfall
  • hybrid model
  • water resources

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation


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