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Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

Shyama Debbarma, Parthasarathi Choudhury, Parthajit Roy, Ram Kumar
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 14
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522566069|DOI: 10.4018/IJAMC.2019010102
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MLA

Debbarma, Shyama, et al. "Analysis of Precipitation Variability using Memory Based Artificial Neural Networks." IJAMC vol.10, no.1 2019: pp.29-42. http://doi.org/10.4018/IJAMC.2019010102

APA

Debbarma, S., Choudhury, P., Roy, P., & Kumar, R. (2019). Analysis of Precipitation Variability using Memory Based Artificial Neural Networks. International Journal of Applied Metaheuristic Computing (IJAMC), 10(1), 29-42. http://doi.org/10.4018/IJAMC.2019010102

Chicago

Debbarma, Shyama, et al. "Analysis of Precipitation Variability using Memory Based Artificial Neural Networks," International Journal of Applied Metaheuristic Computing (IJAMC) 10, no.1: 29-42. http://doi.org/10.4018/IJAMC.2019010102

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Abstract

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

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