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Deterministic Insight into ANN Model Performance for Storm Runoff Simulation

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  • Kwan Lee
  • Wei-Chiao Hung
  • Chung-Chieh Meng

Abstract

The artificial neural network (ANN) theory has been widely applied to practical applications in hydrology. Since watershed rainfall–runoff processes are nonlinear and exhibit spatial and temporal variability, the ANN model, which considers watershed nonlinear characteristics, can usually but not always obtain satisfactory simulation results. The training of an ANN network is based completely on the reliability of the available hydrologic records. The objective of this study was to provide deterministic insight into the limitations of storm runoff simulation when using ANN. Hydrologic records of 42 storm events from two watersheds in Taiwan were adopted for analysis. A deterministic runoff model was used to classify the hydrologic records into “usual” and “unusual” storm events. The analytical results show that the ANN model could provide good simulation results for “usual” storm events; however, its performance was poor when it was applied to “unusual” storm events because no consistent hydrologic characteristics could be extracted from the storm event records using ANN. The success of the ANN model in usual storm discharge simulations may be mainly due to the input vectors including the previous observed discharge. Moreover, the number of past periods of rainfall that were set as the input vectors of the ANN model was found to be highly correlated with the watershed time of concentration. It can be used to efficiently determine the ANN network structure instead of using iterative network training. Copyright Springer Science+Business Media, Inc. 2008

Suggested Citation

  • Kwan Lee & Wei-Chiao Hung & Chung-Chieh Meng, 2008. "Deterministic Insight into ANN Model Performance for Storm Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 67-82, January.
    • Handle: RePEc:spr:waterr:v:22:y:2008:i:1:p:67-82
    DOI: 10.1007/s11269-006-9144-x
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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
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    2. Nariman Valizadeh & Majid Mirzaei & Mohammed Falah Allawi & Haitham Abdulmohsin Afan & Nuruol Syuhadaa Mohd & Aini Hussain & Ahmed El-Shafie, 2017. "Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(3), pages 1377-1392, April.
    3. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    4. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
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    7. Vahid Gholami & Mohammad Reza Khaleghi, 2021. "A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(4), pages 165-174.
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