Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization

Accurate and sustainable management of water resources is among the most important circumstances of basin and river engineering. In this study, a hybrid machine learning (ML) model was generated using CatBoost and Genetic Algorithm (GA) for significant impact on river flow prediction. The study was applied to Sakarya Basin, which is located in semi-arid climatic conditions in Turkey. The forecast performance of the models was observed by developing a day-step ahead forecast scenario with the data of Adatepe, Aktaş and Rüstümköy flow measurement stations (FMS). The daily flow data of the specified stations between 2002 and 2012 were used and the performance of the proposed model was tested by comparing with CatBoost, Long-Short Term Memory (LSTM) and the classical estimation method, Linear Regression (LR). The study was also aimed to improve the predictive performance of genetic algorithms combined with the gradient boosting model (GA-CatBoost). The developed hybrid model outperformed the benchmarked models. The results showed that the developed model can be successfully applied in river flow forecasting.