Water and Irrigation Management

Water and Irrigation Management

Performance evaluation of signal decomposition methods in monthly precipitation estimation (case study: Telezang station)

Document Type : Research Paper

Authors
Nazanin Alivand 1
AliMohammd AkhoondAli 2
Farshad Ahmadi 2
1 Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Water Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
10.22059/jwim.2025.396662.1239
Abstract
Precipitation forecasting is of great importance due to its impact on agriculture, natural disaster management, and water supply. Therefore, in this study, the monthly precipitation of the Telezang station from 1966 to 2020 was modeled using the Variable Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Input data were defined for the Support Vector Machine (SVM) model based on four scenarios. In the first scenario, monthly precipitation values with up to four lags were considered as model inputs. In the second scenario, in addition to the lagged precipitation data, a periodic term was added to the input patterns of the model. In the third and fourth scenarios, the monthly precipitation data were decomposed using CEEMD and VMD, respectively, and provided to the model. The findings of this research indicated that adding the periodic term slightly improved the model’s performance. Additionally, a comparison of the results from the data preprocessing methods using VMD and CEEMD showed that the VMD-SVM model outperformed the CEEMD-SVM model significantly, reducing the MAE index by an average of approximately 35.25 mm compared to the standalone model and 13.77 mm compared to the CEEMD-SVM model, while also achieving greater accuracy.
Keywords
Data preprocessing
Rainfall forecasting
Variable Mode Decomposition
Complete Ensemble Empirical Mode Decompositio
Subjects
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Water and Irrigation Management
Volume 15, Issue 3
Autumn 2025
Pages 525-542