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Article

Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

by 1,† and 2,*,†
1
Department of Soil and Water Conservation, National Pingtung University of Science and Technology, Neipu Shiang 912, Taiwan
2
Management School, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Haw Yen
Water 2021, 13(9), 1241; https://doi.org/10.3390/w13091241
Received: 17 February 2021 / Revised: 23 April 2021 / Accepted: 26 April 2021 / Published: 29 April 2021
This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years. View Full-Text
Keywords: climate change; stochastic model; multi-scale analysis; Markov chain random field; optimal ensemble learning climate change; stochastic model; multi-scale analysis; Markov chain random field; optimal ensemble learning
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MDPI and ACS Style

Lee, M.-H.; Chen, Y.J. Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace. Water 2021, 13, 1241. https://doi.org/10.3390/w13091241

AMA Style

Lee M-H, Chen YJ. Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace. Water. 2021; 13(9):1241. https://doi.org/10.3390/w13091241

Chicago/Turabian Style

Lee, Ming-Hsi, and Yenming J. Chen. 2021. "Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace" Water 13, no. 9: 1241. https://doi.org/10.3390/w13091241

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