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Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model

Author

Listed:
  • T. Reshma

    (Koneru Lakshmaiah Education Foundation)

  • K. Venkata Reddy

    (National Institute of Technology)

  • Deva Pratap

    (National Institute of Technology)

  • V. Agilan

    (National Institute of Technology)

Abstract

The calibration of an event based rainfall-runoff model for steam flow forecasting is challenging because, it is difficult to measure the parameters physically on the field for each rainfall event. In the present study, Fuzzy rule based Multi-objective Genetic Algorithm (MGA) is developed to optimize the infiltration and roughness parameters of an event based rainfall-runoff model. Nash Sutcliffe Efficiency (NSE), Coefficient of Determination (R2) and transformed volume difference (f(V)) are used as the objective functions of the MGA and all Pareto optimal solutions are identified using Nondominated Sorting method. As three objective functions are included in the calibration, the number of Pareto optimal solutions are also increases and hence, the optimization problem now becomes a decision making problem. Therefore, to select the best solution from all Pareto optimal solutions, a Fuzzy Rule-Based Model (FRBM) is developed to get alternative values of each Pareto optimal solution. First, the Fuzzy rule based MGA is developed by integrating the FRBM with the MGA. Then the Fuzzy rule based MGA is integrated with an event based runoff model. The developed Fuzzy-MGA based runoff model is tested on three different watersheds and the simulation results of Fuzzy-MGA based runoff model are compared with observed data and previous study results. From the simulated events of three watersheds using Fuzzy-MGA based runoff model, it is observed that the mean percentage error in any criteria (i.e. volume of runoff, peak runoff, and time to peak) of the developed model for a watershed is less than 16.33%. It is also noted that the developed Fuzzy-MGA based runoff model is able to produce hydrographs that are much closer to the measured hydrographs.

Suggested Citation

  • T. Reshma & K. Venkata Reddy & Deva Pratap & V. Agilan, 2018. "Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1501-1516, March.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:4:d:10.1007_s11269-017-1884-2
    DOI: 10.1007/s11269-017-1884-2
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    References listed on IDEAS

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    1. D. Regulwar & P Raj, 2008. "Development of 3-D Optimal Surface for Operation Policies of a Multireservoir in Fuzzy Environment Using Genetic Algorithm for River Basin Development and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(5), pages 595-610, May.
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    Cited by:

    1. Hu Caihong & Zhang Xueli & Li Changqing & Liu Chengshuai & Wang Jinxing & Jian Shengqi, 2022. "Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 103-117, January.
    2. Sanjeet Kumar & Ashok Mishra & Umesh Kumar Singh, 2023. "Assessment of Land Cover Changes and Climate Variability Effects on Catchment Hydrology Using a Physically Distributed Model," Sustainability, MDPI, vol. 15(13), pages 1-15, June.

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