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Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran

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Abstract

Snow water equivalent (SWE) is a key parameter in hydrological cycle, and information on regional SWE is required for various hydrological and meteorological applications, as well as for hydropower production and flood forecasting. This study compares the snow depth and SWE estimated by multivariate linear regression (MLR), discriminant function analysis, ordinary kriging, ordinary kriging-multivariate linear regression, ordinary kriging-discriminant function analysis, artificial neural network (ANN) and neural network-genetic algorithm (NNGA) models. The analysis was performed in the 5.2 km2 area of Samsami basin, located in the southwest of Iran. Statistical criteria were used to measure the models’ performances. The results indicated that NNGA, ANN and MLR methods were able to predict SWE at the desirable level of accuracy. However, the NNGA model with the highest coefficient of determination (R 2 = 0.70, P value < 0.05) and minimum root mean square error (RMSE = 0.202 cm) provided the best results among the other models. The lower SWE values were registered in the east of study area and higher SWE values appeared in the west of study area where altitude was higher.

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Acknowledgments

The authors express their gratitude to Bu-Ali Sina University for its supports. Special thanks are due to the different people who measured the required parameters. The efforts of the anonymous reviewers in improving the article also appreciated.

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Authors and Affiliations

  1. Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, 65174, Iran

    Hossein Tabari, S. Marofi & H. Zare Abyaneh

  2. Department of Civil Engineering, Faculty of Engineering, Jondi-Shapour University, Dezful, Iran

    M. R. Sharifi

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  1. Hossein Tabari
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Correspondence to Hossein Tabari.

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Tabari, H., Marofi, S., Abyaneh, H.Z. et al. Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput & Applic 19, 625–635 (2010). https://doi.org/10.1007/s00521-009-0320-9

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  • DOI: https://doi.org/10.1007/s00521-009-0320-9

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