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Artificial Neural Network Modelling for Simulating Catchment Runoff: A Case Study of East Melbourne

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Proceedings of World Conference on Artificial Intelligence: Advances and Applications (WWCA 1997)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The highly complex topography and inherent nonlinearity associated with influential hydrological processes of urban catchments, coupled with limited data availability, limit the prediction accuracy of conventional hydrologic models. Artificial Neural Network (ANN) models have displayed commendable progress in their capability to recognise and simulate highly complex, nonlinear relationships between input and output variables, without needing to understand the underlying physical processes. This paper presents the effectiveness of ANN modelling technique in accurately predicting catchment runoff, based only on rainfall and flow data. Gardiners Creek catchment, located in eastern Melbourne, was selected as the study area, where over 44 years of quality-checked rainfall and flow data, at six-minute time intervals, recorded at the nearest gauging stations, were provided by Melbourne Water Corporation. The data for two of the most recent storm events that transpired within the study area over the last decade—4 February 2011 and 6 November 2018, were selected for calibrating, validating and evaluating the effectiveness of the developed ANN model in accurate estimation of the catchment runoff. The results from the study suggest that the use of ANN provides accurate estimates of historical storm events and can be considered suitable for application in studies of urban catchment responses, with limited data availability.

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Author information

Authors and Affiliations

  1. Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Australia

    Harshanth Balacumaresan, Md. Abdul Aziz & Monzur Imteaz

  2. School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Churchill, VIC, 3353, Australia

    Tanveer Choudhury

Authors
  1. Harshanth Balacumaresan
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  2. Md. Abdul Aziz
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  3. Tanveer Choudhury
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  4. Monzur Imteaz
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Corresponding author

Correspondence to Harshanth Balacumaresan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

    Ashish Kumar Tripathi

  2. Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, India

    Darpan Anand

  3. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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Balacumaresan, H., Aziz, M.A., Choudhury, T., Imteaz, M. (2023). Artificial Neural Network Modelling for Simulating Catchment Runoff: A Case Study of East Melbourne. In: Tripathi, A.K., Anand, D., Nagar, A.K. (eds) Proceedings of World Conference on Artificial Intelligence: Advances and Applications. WWCA 1997. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5881-8_9

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