A multivariate decomposition–ensemble model for estimating long-term rainfall dynamics-Reference-Cited by-同舟云学术

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A multivariate decomposition–ensemble model for estimating long-term rainfall dynamics

Published:2022-12-29 Issue: Volume: Page:
ISSN:0930-7575
Container-title:Climate Dynamics
language:en
Short-container-title:Clim Dyn

Author:

Narimani Roya,Jun Changhyun,Saedi Alireza,Bateni Sayed M.,Oh Jeill

Funder

Korean Environment Industry & Technology Institute

National Research Foundation of Korea

Chung-Ang University

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science

Link

https://link.springer.com/content/pdf/10.1007/s00382-022-06646-x.pdf

Reference68 articles.

1. Abdel-Kader H, Abd-El Salam M, Mohamed M (2021) Hybrid machine learning model for rainfall forecasting. J Intell Syst Internet Things 1(1):5–12. https://doi.org/10.5281/zenodo.3376685

2. Akiner ME (2021) Long-term rainfall information forecast by utilizing constrained amount of observation through artificial neural network approach. Adv Meteorol. https://doi.org/10.1155/2021/5524611

3. Anwar MT, Winarno E, Hadikurniawati W, Novita M (2021) Rainfall prediction using Extreme Gradient Boosting. J Phys Conf Ser 1869:012078. https://doi.org/10.1088/1742-6596/1869/1/012078

4. Apaydin H, Sattari MT, Falsafian K, Prasad R (2021) Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions. J Hydrol 600:126506. https://doi.org/10.1016/j.jhydrol.2021.126506

5. Ba H, Guo S, Wang Y, Hong X, Zhong Y, Liu Z (2018) Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques. Hydrol Res 49(3):744–760. https://doi.org/10.2166/nh.2017.048

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