Combining deep learning methods and multi-resolution analysis for drought forecasting modeling
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01009-4.pdf
Reference41 articles.
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2. Bazrafshan J, Khalili A (2013) Spatial analysis of meteorological drought in Iran from 1965 to 2003. Desert 18(1):63–71. https://doi.org/10.22059/jdesert.2013.36276
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4. Beguería S, Vicente-Serrano SM, Angulo-Martínez M (2010) A multiscalar global drought dataset: the SPEIbase: a new gridded product for the analysis of drought variability and impacts. Bulletin of the American Meteorological Society 91(10):1351–1354. http://www.jstor.org/stable/26233020. Accessed 12 Mar 2023
5. Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):6085. https://doi.org/10.1038/s41598-018-24271-9
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