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Single- and two- step optimization of infiltration parameters and Manning’s roughness coefficients for a watershed using a multi-objective genetic algorithm
- Reshma, T., Reddy, K. Venkata, Pratap, Deva, Agilan, V.
- ISH journal of hydraulic engineering 2018 v.24 no.1 pp. 53-60
- algorithms, correlation, models, rain, roughness, runoff, system optimization, watersheds, India
- In the present study, two optimization methods, i.e. single-step optimization (SS) and two-step optimization with varying parameter range (TSVR) have been developed for optimizing four infiltration and two roughness coefficients of an event-based distributed rainfall run-off model using a multi-objective genetic algorithm (MGA). For this, an event-based rainfall run-off model is integrated with MGA to evaluate two optimization methods. Correlation coefficient (r) and Nash–Sutcliffe efficiency (NSE) are used as objective functions of MGA. Further, the MGA integrated run-off model has been tested on the Harsul watershed located in Maharashtra, India and the simulated results are compared with the observed data. From the simulation results of SS optimization method, it is observed that the average percentage error of the volume of run-off, peak run-off and time to peak are 40.31%, 23.44% and 22.74%, respectively. In case of TSVR optimization method, the average percentage error of the volume of run-off, peak run-off and time to peak are 38.91%, 14.34% and 29.23%, respectively. Results of this study indicate that the TSVR optimization method reduced the average percentage error in volume of run-off and peak run-off and increased the average percentage error of time to peak when compared to the SS optimization method.