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Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks

Author

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  • J. Drisya

    (National Institute of Technology)

  • D. Sathish Kumar

    (National Institute of Technology)

  • Thendiyath Roshni

    (National Institute of Technology)

Abstract

In semi-arid watersheds, hydrological drought is manifested by reasonably low streamflow conditions. This makes streamflow forecasting as an inevitable component for implementing drought management practices. Data-driven modelling techniques are often applied for simulating the streamflow forecasts. In this study, a comparison between conventional feedforward neural network (FFNN) model and wavelet enabled artificial neural network (WANN) model is carried out to analyse their effectiveness in streamflow forecasting. The input data used to develop and simulate the models are monthly precipitation, and monthly river stage of twenty-five years (January 1991 to December 2015). Data pre-processing is carried out using correlation analysis prior to neural network modelling for selecting appropriate input combinations. The preprocessed data is directly given as input for FFNN; whereas for WANN, the preprocessed time series datasets are decomposed into several sub-series and are used as the inputs. Analysis on three different transfer functions that are commonly used in ANN models is carried out to identify the best transfer function. Hyperbolic tangent sigmoid transfer function is found to be best suitable for modelling streamflow forecasts. The result also shows that there is a significant improvement in streamflow forecasting ability for WANN models compared to FFNN. Drought forecasting is carried out by developing a standardized streamflow index from the forecasted streamflow. The drought forecasting technique discussed here will help planners to make informed decisions on watershed management and drought mitigation measures.

Suggested Citation

  • J. Drisya & D. Sathish Kumar & Thendiyath Roshni, 2021. "Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3653-3672, March.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:3:d:10.1007_s10668-020-00737-7
    DOI: 10.1007/s10668-020-00737-7
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    References listed on IDEAS

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    1. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    2. Samane Saadat & Davar Khalili & Ali Kamgar-Haghighi & Shahrokh Zand-Parsa, 2013. "Investigation of spatio-temporal patterns of seasonal streamflow droughts in a semi-arid region," 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. 69(3), pages 1697-1720, December.
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    Cited by:

    1. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    2. Salem Gharbia & Khurram Riaz & Iulia Anton & Gabor Makrai & Laurence Gill & Leo Creedon & Marion McAfee & Paul Johnston & Francesco Pilla, 2022. "Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale," Sustainability, MDPI, vol. 14(7), pages 1-23, March.

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