Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning-Reference-Cited by-同舟云学术

Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning

Published:2021-07-29 Issue:8 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Res

Author:

Li Jun¹²,Wang Zhaoli¹²,Wu Xushu¹²^ORCID,Xu Chong‐Yu³^ORCID,Guo Shenglian⁴,Chen Xiaohong⁵^ORCID,Zhang Zhenxing⁶

Affiliation:

1. School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science South China University of Technology Guangzhou China

2. Guangdong Engineering Technology Research Center of Safety and Greenization for Water Conservancy Project Guangzhou China

3. Department of Geosciences University of Oslo Oslo Norway

4. State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan China

5. Center for Water Resource and Environment Sun Yat‐Sen University Guangzhou China

6. Illinois State Water Survey Prairie Research Institute University of Illinois Champaign IL USA

Funder

Water Resources Department of Guangdong Province

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029413

Reference72 articles.

1. A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

2. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

3. Living with drought in South Africa: lessons learnt from the recent El Niño drought period

4. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring

5. Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

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