A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir

Abstract

Abstract Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs. This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center‐Hydrologic Modeling System (HEC‐HMS) hydrological model and an Encoder‐Decoder‐Long Short‐Term Memory network to enhance the accuracy of real‐time flood forecasting. The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir. The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate, validate, and test the hybrid model. Results show that the HEC‐HMS‐artificial neural network hybrid model significantly improves the forecast quality, especially for results at a longer forecasting time. In detail, the Kling–Gupta efficiency (KGE) index, for example, increased from ∆ KGE = 16% at time t + 1 h to ∆ KGE = 69% at time t + 6 h. Similar results were obtained for other indicators including peak error and volume error. The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning, flood control, and water resource efficiency.