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Data on optimization of the non-linear Muskingum flood routing in Kardeh River using Goa algorithm.
Saeid Khalifeh1, Kazem Esmaili2, SaeedReza Khodashenas2
1Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
Data in Brief
|March 28, 2020
Summary
This study optimized flood routing for the Kardeh River using the Grasshopper Optimization Algorithm (GOA). GOA demonstrated superior performance in non-linear Muskingum flood routing compared to Genetic Algorithm and Harmony Search.
Area of Science:
- Hydrology
- Computational Fluid Dynamics
- Environmental Engineering
Background:
- Flood routing is crucial for water resource management and disaster prevention.
- The Non-linear Muskingum model is widely used but requires accurate parameter optimization.
- Kardeh River in Northeastern Iran presents a case study for hydrological modeling.
Purpose of the Study:
- To optimize the Non-linear Muskingum flood routing model for the Kardeh River.
- To evaluate the performance of the Grasshopper Optimization Algorithm (GOA) against other metaheuristic algorithms.
- To provide time-series data for hydrological research and model development.
Main Methods:
- Utilized time-series data including river inflow, storage volume, and river outflow.
- Developed a Non-linear Muskingum flood routing model optimized by the Grasshopper Optimization Algorithm (GOA).
- Compared GOA with Genetic Algorithm (GA) and Harmony Search (HS) for parameter optimization.
Main Results:
- The Grasshopper Optimization Algorithm (GOA) achieved the best solution value of 3.53.
- Genetic Algorithm (GA) and Harmony Search (HS) obtained solution values of 5.29 and 5.69, respectively.
- GOA demonstrated superior performance in optimizing the Non-linear Muskingum flood routing model.
Conclusions:
- The Grasshopper Optimization Algorithm (GOA) is highly effective for optimizing the Non-linear Muskingum flood routing model.
- GOA offers a significant improvement over GA and HS for hydrological flood routing applications.
- The findings contribute to enhanced flood management strategies through advanced computational methods.
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