Development of Multi-Inflow Prediction Ensemble Model Based on Auto-Sklearn Using Combined Approach: Case Study of Soyang River Dam

Abstract

Accurate prediction of dam inflows is essential for effective water resource management and dam operation. In this study, we developed a multi-inflow prediction ensemble (MPE) model for dam inflow prediction using auto-sklearn (AS). The MPE model is designed to combine ensemble models for high and low inflow prediction and improve dam inflow prediction accuracy. We investigated the impact of datasets assigned to flow regimes on the ensemble composition and compared the performance of the MPE model to an AS-based ensemble model developed using a conventional approach. Our findings showed that the MPE model outperformed the conventional model in predicting dam inflows during flood and nonflood periods, reducing the root mean square error (RMSE) and mean absolute error (MAE) by 22.1% and 24.9% for low inflows, and increasing the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) by 21.9% and 35.8%, respectively. These results suggest that the MPE model has the potential to improve water resource management and dam operation, benefiting both the environment and society. Overall, the methodology of this study is expected to contribute to the development of a robust ensemble model for dam inflow prediction in regions with high climate variability.

A Review of Machine Learning Techniques in Agroclimatic Studies

“…Innovations such as Auto-sklearn [58], Tree-based Pipeline Optimization Tool (TPOT) [59], and H2O AutoML [60,61] represent groundbreaking advancements in automating this process, particularly through hyperparameter optimization and NAS. These tools can be specialized in refining ML and DL applications in agriculture by streamlining the selection and tuning of models to enhance performance.…”

“…TPOT advances this automation using genetic programming, iteratively evolving machine learning pipelines for optimal performance, thereby simplifying the development of robust models for complex challenges [59,62]. H2O AutoML further democratizes ML application, offering an accessible interface for exploring diverse models and employing model stacking for superior predictive accuracy, invaluable in rapid model deployment scenarios [60,61]. In addition, these approaches significantly reduce the barrier to entry for applying sophisticated ML models, enabling more accurate and informed decisions without requiring users to have in-depth algorithmic understanding [63].…”

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

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