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Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

1
Department of Water Engineering and Hydraulic Structure, Semnan University, Semnan 35131, Iran
2
Department of Geography, Razi University, Kermanshah 67146, Iran
3
Department of Meteorology and Geophysics, University of Vienna, Vienna 1090, Austria
4
Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences (BOKU), Vienna 1180, Austria
5
Department of Water Resource Management, Razi University, Kermanshah 67146, Iran
6
Department of Environmental Geosciences, University of Vienna, Vienna 1090, Austria
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1653; https://doi.org/10.3390/w11081653
Received: 23 June 2019 / Revised: 7 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
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Abstract

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms. View Full-Text
Keywords: optimize artificial neural networks; downscaling; satellite precipitation monitoring; cloud optical and microphysical properties optimize artificial neural networks; downscaling; satellite precipitation monitoring; cloud optical and microphysical properties
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Salimi, A.H.; Masoompour Samakosh, J.; Sharifi, E.; Hassanvand, M.R.; Noori, A.; von Rautenkranz, H. Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data. Water 2019, 11, 1653.

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