Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types-Reference-Cited by-同舟云学术

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Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types

Published:2021-11-05 Issue: Volume: Page:
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Author:

Wang Jiayi¹^ORCID,Wong Raymond K. W.¹,Jun Mikyoung²^ORCID,Schumacher Courtney¹^ORCID,Saravanan R³,Sun Chunmei²

Affiliation:

1. Texas A&M University

2. University of Houston

3. Department of Atmospheric Sciences, Texas A & M University

Publisher

Wiley

Reference39 articles.

1. The Cumulus Parameterization Problem: Past, Present, and Future;Arakawa A.;Journal of Climate,2004

2. Ardakani A. C. Condo and W. J. Gross (2016). Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks. arXiv preprint arXiv:1611.01427 .

3. Baldi P. and P. J. Sadowski (2013). Understanding Dropout. Advances in neural information processing systems 26 2814-2822.

4. Breiman L. (2001). Random Forests. Machine learning 45 (1) 5-32.

5. Prognostic Validation of A Neural Network Unified Physics Parameterization;Brenowitz N. D.;Geophys. Res. Lett.,2018

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