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Monthly surface runoff prediction using artificial intelligence: A study from a tropical climate river basin

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Abstract

Accurate surface runoff prediction is vital for water resources engineers for various applications. Advances in the artificial intelligence techniques can act as robust tools for modelling hydrological processes. The present study focuses on testing the reliability of different data sources and choosing the correct source to model the rainfall-runoff process under data scarce situations using AI techniques. In this study, an absolute homogeneity test was performed for TRMM, gridded and observed precipitation data and found that the observed precipitation dataset is homogeneous and best suitable for modelling rainfall-runoff process in Kallada river basin, Kerala. Emotional artificial neural network (EANN) is a novel hybrid neural network and it is suggested in the present study for accurate monthly surface runoff prediction. This study was also conceived to address and investigate the efficiency of EANN for forecasting monthly surface runoff and compare the performances with conventional feed forward neural network (FFNN) and multivariate adaptive regression spline (MARS) models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination (R2) and graphical indicators are used for assessing the efficacy of the developed models. The results showed that the EANN model performs better with R2 = 0.80 for the training phase and R2 = 0.77 for validation phase compared to other models. The improvement in the performance of EANN model over FFNN model is 12% and 5.8% for coefficient of determination in the training and validation phase, respectively. Further, the Taylor diagram indicates that there is a close match between the observed and EANN model predicted values in terms of statistical parameters. Overall, this study demonstrated the effectiveness of EANN in modelling the rainfall-runoff process and also could be a useful technique in other fields of water resources engineering.

Highlights

  • The present study focuses on testing the reliability of different data sources such as gridded, observed and TRMM precipitation datasets and choosing the correct source to model the rainfall-runoff process using AI techniques.

  • From the selected homogeneous dataset and the observed runoff data, potential predictors were identified based on correlation analysis and partial autocorrelation function (PACF).

  • The monthly runoff prediction models were developed using three AI techniques namely FFNN, MARS and EANN in a tropical river basin (Kallada) of Kerala with scarce amount of data.

  • The performance of the developed models were assessed using statistical indicators (NSE, RMSE, and R2) and graphical indicators (Taylor diagram, REC plots and Random walk test).

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Acknowledgements

Authors would like to acknowledge Central Water Commission (CWC) and India Meteorological Department (IMD) for providing sufficient data. The authors also acknowledge the anonymous reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India

    Beeram Satya Narayana Reddy & S K Pramada

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Thendiyath Roshni

Authors
  1. Beeram Satya Narayana Reddy
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  2. S K Pramada
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  3. Thendiyath Roshni
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Contributions

Beeram Satya Narayana Reddy: Formal assessment, conceptualization, data collection, framework of methodology, model development, software, initial draft writing, review and editing. Pramada S K: Formal assessment, conceptualization, methodology, resources, supervision and validation. Thendiyath Roshni: Formal assessment, conceptualization, methodology, resources, supervision and validation.

Corresponding author

Correspondence to S K Pramada.

Additional information

Communicated by Rajib Maity

Appendix: Developed models

Appendix: Developed models

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Reddy, B.S.N., Pramada, S.K. & Roshni, T. Monthly surface runoff prediction using artificial intelligence: A study from a tropical climate river basin. J Earth Syst Sci 130, 35 (2021). https://doi.org/10.1007/s12040-020-01508-8

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  • DOI: https://doi.org/10.1007/s12040-020-01508-8

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