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Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

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  • Mohammad Rezaie-Balf

    (Graduate University of Advanced Technology-Kerman)

  • Zahra Zahmatkesh

    (Graduate University of Advanced Technology-Kerman)

  • Sungwon Kim

    (Dongyang University)

Abstract

Accurate simulation of rainfall-runoff process is of great importance in hydrology and water resources management. Rainfall–runoff modeling is a non-linear process and highly affected by the inputs to the simulation model. In this study, three kinds of soft computing methods, namely artificial neural networks (ANNs), model tree (MT) and multivariate adaptive regression splines (MARS), have been employed and compared for rainfall-runoff process simulation. Moreover, this study investigates the effect of input size, including number of input variables and number of data time series on runoff simulation by the developed models. Inputs to the simulation models for calibration and validation purposes consist two parts: I1: five variables, including daily rainfall and runoff time series (30 years) with lag times, and I2: twelve variables, including daily rainfall and runoff time series (10 years). To increase the model performances, optimal number and type for input variables are identified. The efficiency of the training and testing performances using the ANNs, MT and MARS models is then evaluated using several evaluation criteria. To implement the methodology, Tajan catchment in the northern part of Iran is selected. Based on the results, it was found that using I1 as input to the developed models results in higher simulation performance. The results also provided evidence that MT (R = 0.897, RMSE = 6.70, RSE = 0.33) with set I2 is capable of reliable model for rainfall-runoff process compared with MARS (R = 0.892, RMSE = 7.47, RSE = 0.83) and ANNs (R = 0.884, RMSE = 7.40, RSE = 0.43) models. Therefore, size (length of data time series) and type of input variables have significant effects on the modeling results.

Suggested Citation

  • Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:12:d:10.1007_s11269-017-1711-9
    DOI: 10.1007/s11269-017-1711-9
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    3. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
    4. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    5. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
    6. Gaurav Singh & A. R. S. Kumar & R. K. Jaiswal & Surjeet Singh & R. M. Singh, 2022. "Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 12311-12339, October.
    7. Amir Molajou & Vahid Nourani & Abbas Afshar & Mina Khosravi & Adam Brysiewicz, 2021. "Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2369-2384, June.

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