Journal Article

Monthly runoff prediction based on variational modal decomposition combined with the dung beetle optimization algorithm for gated recurrent unit model

Chu-Tian, Xu, Liang, Chen, Wen-Chao, Ban, Liang-Duo, Shen

2023

Overview

Highly accurate monthly runoff forecasts play a pivotal role in water resource management and utilization. This article proposes a coupling of variational modal decomposition (VMD) and the dung beetle optimization algorithm (DBO) with the gated recurrent unit (GRU) to establish a new monthly runoff forecasting model: the VMD-DBO-GRU. Initially, historical runoff data are decomposed via VMD. Subsequently, the parameters of the GRU are optimized using the DBO, and the decomposed monthly runoff components are inputted into the GRU neural network. Finally, the predictions for each component are consolidated to provide monthly runoff predictions. The model is then validated using monthly runoff data from the Ansha reservoir in Fujian, collected from 1980 to 2020. The results demonstrate a higher prediction accuracy of the VMD-DBO-GRU model compared to BP, SVM, GRU, VMD-GRU, DBO-GRU, and EMD-GRU models, providing a new alternative for conducting monthly runoff prediction.

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: Accuracy / Algorithms / Animals / Atmospheric Protection/Air Quality Control/Air Pollution / China / Coleoptera / Decomposition / Deep learning / Dung / dung beetles / Earth and Environmental Science / Ecology / Ecotoxicology / Environment / Environmental Management / Environmental Monitoring / Feces / Forecasting / Forecasting models / Hydrologic data / Machine learning / Mathematical models / Modelling / Monitoring/Environmental Analysis / Monthly / Monthly forecasts / Monthly runoff / Neural networks / Neural Networks, Computer / Optimization / Optimization algorithms / prediction / Predictions / Research methodology / Resource management / Runoff / Runoff forecasting / Time series / Water conservation / water management / Water resources / Water resources management