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Artificial neural network coupled with wavelet transform for estimating snow water equivalent using passive microwave data

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Abstract

Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the streamflow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the value of SWE in a mountainous basin. In addition, k-fold cross validation method is used in order to achieve a more reliable and robust model. In this regard, microwave images acquired from Spectral Sensor Microwave Imager (SSM/I) are used to estimate the SWE of Tehran sub-basins during 1992–2008 period. Also for obtaining measured SWE within the corresponding Equal-Area Scalable Earth-Grid (EASE-Grid) cell of SSM/I image, approach of Cell-SWE extraction using height–SWE relations is applied in order to reach more precise estimations. The obtained results reveal that the wavelet-ANN model significantly increases the accuracy of estimations, mainly because of using multi-scale time series as the ANN inputs. The Nash–Sutcliffe Index (NSE) for ANN and WANN models are respectively 0.09 and 0.44 which shows a firm improvement of 0.35 in NSE parameter when WANN is applied. Similar trend is observed in other parameters including RMSE where the value is 0.3 for ANN and 0.07 for WANN.

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Acknowledgments

Authors would like to thank NSIDC for providing SSM/I data and the Iranian Water Resource Management Organization for making available ground data needed in this project.

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Authors and Affiliations

  1. Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

    A B Dariane, S Azimi & A Zakerinejad

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  1. A B Dariane
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  2. S Azimi
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  3. A Zakerinejad
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Correspondence to A B Dariane.

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Dariane, A.B., Azimi, S. & Zakerinejad, A. Artificial neural network coupled with wavelet transform for estimating snow water equivalent using passive microwave data. J Earth Syst Sci 123, 1591–1601 (2014). https://doi.org/10.1007/s12040-014-0485-1

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  • DOI: https://doi.org/10.1007/s12040-014-0485-1

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