Long-term precipitation forecasting for Ilam city using a hybrid machine learning model

Document Type : Original Article

Authors

1 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran.

2 Department of Civil Engineering, Arak Branch , Islamic Azad University, Arak, Iran .

3 Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran .

Abstract

Objective: In this study, we seek to predict long-term precipitation in Ilam city using artificial intelligence algorithms.
 
Method: In this study, the long-term precipitation of Ilam city over a 44-year period from 1980 to 2024 in simulated by a hybrid machine learning model.
 
Ilam Province has a special topographic situation with uneven precipitation distribution due to its location at different latitudes. In this study, after collecting data, the average number of months of rainfall for each of the studied stations for a certain period is obtained. A machine learning model is used for prediction. In the first step, the observed data are normalized and the best normalization coefficients are obtained for this study. Approximately 70% of the observed data are used to train the artificial intelligence models and the remaining 30% are used to test them. Subsequently, the optimal number of hidden layer neurons along with the best activation function of the ORELM model are selected by implementing a trial and error process. In this study, the regularization parameter of the ORELM model is also optimized. Also, using the autocorrelation function (ACF), the effective lags of time series data are identified and using them, fourteen WORELM models are developed.
 
Results: According to the simulation results, the correlation coefficient (R) values for the ELM and ORELM models are obtained as 0.618 and 0.952, respectively. While the NSC values for ELM and ORELM are estimated as -1.137 and -1.138, respectively.  In other words, the ORELM model shows more accuracy compared to the ELM model. In addition, the ORELM hybrid learning machine model is identified as the best model for simulating precipitation values in Ilam city.
 
Conclusions: The results of the ORELM hybrid model were compared with the ELM and ORELM artificial intelligence models, which showed that the ORELM hybrid model performed better.

Keywords

Main Subjects

References

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Advanced Technologies in Water Efficiency
Volume 5, Issue 2
June 2025
Pages 116-133

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History

  • Receive Date: 29 November 2024
  • Revise Date: 31 January 2025
  • Accept Date: 18 April 2025

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