Monthly Volumes of Water Inflow into the Large Dam Reservoirs in Eastern Thailand Forecasting by The Cuckoo Search Optimization Enhanced Decomposition and Holt-Winters Techniques
Keywords:
forecasting, dam reservoir, cuckoo search optimization, decomposition, Holt-WintersAbstract
This study aims to evaluate the efficiency of models through the integration of Cuckoo Search Optimization (CS) with Holt-Winters (CS-HW) and Decomposition (CS-D) for forecasting monthly inflow volumes into large dam reservoirs in Eastern Thailand, covering a total of 6 dams. This is compared with the Grid Search of Holt-Winters (Grid-HW) and the Classic Decomposition Model (Classic-D) using a training dataset spanning 66 months, and employing the Mean Absolute Error (MAE) as the criterion for the lowest MAE to assess model performance. The findings indicate that both CS-HW and CS-D models outperform traditional models, with CS-D demonstrating a significantly lower MAE than Classic-D, while CS-HW shows marginally lower MAE values than Grid-HW across all dams.
For the long-term forecasting of dam reservoir inflow volumes over a 24-month horizon using a test dataset, criteria including Root Mean Square Error (RMSE), MAE, and Symmetric Mean Absolute Percentage Error (sMAPE) were utilized to evaluate model performance. The results show that for different dams, specific models were chosen as best fitting: Khun Dan Prakan Chon Dam selected the CS with Additive Holt-Winters (CS-HW+), Khlong Siyat chose the CS with Multiplicative Holt-Winters (CS-HWx), Naruebodindrashinta Dam opted for the Box-Jenkins model, Bang Phra Dam for the CS with Multiplicative Decomposition (CS-Dx), Prasae Dam for the Grid Search of Multiplicative Holt-Winters (Grid-HWx), and Nong Pla Lai for the Grid Search of Additive Holt-Winters (Grid-HW+). The outcome of the 24-month forecasting demonstrates clear seasonal patterns in the reservoir inflow volumes to the dams.
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