Scholarly record
ELEPHANT HERDING OPTIMIZATION FOR ENHANCED FORECASTING OF INFLOW TIME SERIES USING STATISTICAL AND MACHINE LEARNING MODELS
Abstract
Accurate forecasting of reservoir inflows based on historical data is essential for effective water resources planning and management. Water flow forecasting presents a major challenge due to the nonlinear, non-stationary, stochastic, and highly noisy nature of water flows in rivers and inflows into reservoirs. These complex characteristics hinder the modeling process and limit the effectiveness of conventional prediction methods, making it difficult to achieve high accuracy results. This study explores the use of the Elephant Herding Optimization (EHO) algorithm for hyperparameter tuning in three established forecasting models: Autoregressive Integrated Moving Average (ARIMA), Gated Recurrent Unit (GRU), and Random Forest (RF). The proposed framework enables efficient exploration of the parameter space and adaptive learning of inflow patterns, aiming to reduce overfitting and improve predictive accuracy. All models are trained using historical inflow data and evaluated for one-day-ahead forecasts over a 365-day period in an open-loop configuration. By combining data-driven methods with meta-heuristic optimization, this work contributes to the development of robust forecasting tools for water resources management, enhancing the resilience and sustainability of hydropower system operation.
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