Simulation of reservoir outflows using regression tree and support vector machine

Abstract

Water stored in reservoirs has a lot of crucial function, including generating hydropower, supporting water supply, and relieving lasting droughts. During floods, water deliveries from reservoirs must be acceptable, so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding downstream. This study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning technology. As a new and exciting AI area, this technology is regarded as the most valuable, time-saving, supervised and cost-effective approach. In this study, two data-driven forecasting models, i.e., Regression Tree (RT) and Support Vector Machine (SVM), were employed for approximately 30 years’ hydrological records, so as to simulate reservoir outflows. The SVM and RT models were applied to the data, accurately predicting the fluctuations in the water outflows of a Bhakra reservoir. Different input combinations were used to determine the most effective release. For cross-validation, the number of folds varied. It is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE, maximum R2, and minimum MAE; therefore, it can be considered as the best model for the dataset used in this study.