Real-Time Operation of Reservoirs during Flood Conditions Using Optimization-Simulation with One- and Two-Dimensional Modeling

Albo-Salih, Hasan; Mays, Larry W.; Che, Daniel

doi:10.1201/9780429463938-18

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Which method to use is determined by the amount of data available in each case study (Beven, 2010). The physical‐based models that have been widely studied and applied in practice, such as MIKE‐NAM (by DHI, Denmark), The Soil & Water Assessment Tool (SWAT), Hydrologic Engineering Center‐Hydrologic Modeling System (HEC‐HMS), and so on, have shown the feasibility of simulating and forecasting the rainfall–runoff scenario (Albo‐Salih et al, 2022; Chu & Steinman, 2009; Martin et al, 2012). However, these models often require a variety of data types (e.g., topographic, geomorphological, meteorological, and hydrological data) and in‐depth modeling expertise to establish relevant databases on the rainfall–runoff characteristics of each study basin.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen,

Nguyen

2024

River

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%