Boosting Medium-Size Reservoir Water Level Predictions using Cyclical Encoding - Luangmaneerote, Sakchan; Thasana, Wiroj (Engineering, Technology & Applied Science Research, 2025)
Effective feature engineering is crucial for machine learning models to capture complex data patterns. This study explores cyclical encoding, a novel technique designed to enhance the performance of machine learning algorithms on datasets with inherent periodicity. This study addresses the challenge faced by medium-sized reservoirs that lack modern data collection equipment by applying cyclical encoding to improve predictive models. Using medium-sized reservoir data, cyclical encoding was applied to enhance the predictive capabilities of models including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), Linear Regression (LR), and Artificial Neural Networks (ANN). The results show that cyclical encoding significantly enhances the ability of nonlinear models to capture cyclical patterns, reducing error metrics by up to 75%. However, LR showed minimal improvement due to its inherent linear limitations. Feature importance analysis identified cumulative outflow and inflow volumes as key predictors. These findings highlight the vital role of advanced feature-engineering techniques such as cyclical encoding in boosting the accuracy and robustness of nonlinear machine learning models, especially for medium-sized reservoirs without modern equipment. This study underscores its potential for broader applications in domains with periodic data, such as climate modeling and financial time series.