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Water
Volume 18
Issue 11
10.3390/w18111264
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Article

Wavelet-Fourier Network Combined with Advanced Preprocessing Techniques for Univariate Daily Rainfall Prediction

by
Md. Jobayer Parvez Ratul
1,
Usmi Akter
1,
Tajrian Mollick
2,
Eshrat Jahan Mumu
1,
Nondita Deb Nath
1,
Syeda Wasifa Adila
1,
Wafa Saleh Alkhuraiji
3,
Padam Jee Omar
4 and
Mohamed Zhran
5,*
1
Department of Civil Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
2
Institute of Energy, University of Dhaka, Dhaka 1000, Bangladesh
3
Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4
Department of Civil Engineering, UIET, Babasaheb Bhimrao Ambedkar University, Lucknow 226025, India
5
Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
*
Author to whom correspondence should be addressed.
Water 2026, 18(11), 1264; https://doi.org/10.3390/w18111264 (registering DOI)
Submission received: 30 March 2026 / Revised: 13 May 2026 / Accepted: 20 May 2026 / Published: 23 May 2026
(This article belongs to the Special Issue Climate Change and Hydrological Processes, 3rd Edition)
Versions Notes

Abstract

Rainfall prediction is essential for the enhanced understanding of several issues related to water resources and agriculture, such as flood and drought alerts and flood management. Neural network models are frequently used due to their capability of effectively handling large datasets and addressing the non-stationarity of rainfall data series, resulting in better accuracy and affordable solutions. However, further study is necessary to comprehend the dynamic nature and extreme events of rainfall. Therefore, we implemented a novel wavelet Fourier-enhanced network (W-FENet) that included a Fourier enhancement module (FEMEX) and an improved U-Net mechanism to strengthen the predictive accuracy of daily rainfall. The adopted U-Net structure facilitated efficient multiscale feature extraction and preservation of temporal rainfall information through encoder–decoder connections and residual learning. The results of the developed models for one-day-ahead rainfall prediction were evaluated against two traditional neural network models, i.e., artificial neural networks and long short-term memory networks. Mongla, being a coastal station and having a highly non-linear rainfall pattern, operated by the Bangladesh Meteorological Department, was selected as the study area. Four preprocessing techniques were incorporated to enhance the robustness of the models: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and successive variational mode decomposition (SVMD). The SVMD-enhanced W-FENet model (abbreviated as W5) demonstrated significant improvements over existing literature with RMSE = 2.226 mm, MAE = 1.131 mm, PCC = 0.988, NSE = 0.974, and WI = 0.993 at the testing phase.
Keywords: rainfall prediction; W-FENet; signal decomposition; Fourier enhancement; SVMD; neural networks; computational cost rainfall prediction; W-FENet; signal decomposition; Fourier enhancement; SVMD; neural networks; computational cost

Share and Cite

MDPI and ACS Style

Ratul, M.J.P.; Akter, U.; Mollick, T.; Mumu, E.J.; Nath, N.D.; Adila, S.W.; Alkhuraiji, W.S.; Omar, P.J.; Zhran, M. Wavelet-Fourier Network Combined with Advanced Preprocessing Techniques for Univariate Daily Rainfall Prediction. Water 2026, 18, 1264. https://doi.org/10.3390/w18111264

AMA Style

Ratul MJP, Akter U, Mollick T, Mumu EJ, Nath ND, Adila SW, Alkhuraiji WS, Omar PJ, Zhran M. Wavelet-Fourier Network Combined with Advanced Preprocessing Techniques for Univariate Daily Rainfall Prediction. Water. 2026; 18(11):1264. https://doi.org/10.3390/w18111264

Chicago/Turabian Style

Ratul, Md. Jobayer Parvez, Usmi Akter, Tajrian Mollick, Eshrat Jahan Mumu, Nondita Deb Nath, Syeda Wasifa Adila, Wafa Saleh Alkhuraiji, Padam Jee Omar, and Mohamed Zhran. 2026. "Wavelet-Fourier Network Combined with Advanced Preprocessing Techniques for Univariate Daily Rainfall Prediction" Water 18, no. 11: 1264. https://doi.org/10.3390/w18111264

APA Style

Ratul, M. J. P., Akter, U., Mollick, T., Mumu, E. J., Nath, N. D., Adila, S. W., Alkhuraiji, W. S., Omar, P. J., & Zhran, M. (2026). Wavelet-Fourier Network Combined with Advanced Preprocessing Techniques for Univariate Daily Rainfall Prediction. Water, 18(11), 1264. https://doi.org/10.3390/w18111264

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