A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors-Reference-Cited by-同舟云学术

A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors

Published:2021-11 Issue:11 Volume:35 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Zhou Zhengzheng¹^ORCID,Ren Jie²,He Xiaogang³⁴,Liu Shuguang¹

Affiliation:

1. Department of Hydraulic Engineering Tongji University Shanghai China

2. Algorithm & Technology Development Department Huawei Technologies, Co., Ltd. Shanghai China

3. Department of Civil and Environmental Engineering National University of Singapore Kent Ridge Singapore

4. Water in the West, Woods Institute for the Environment Stanford University Stanford California USA

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14424

Reference98 articles.

1. Application of artificial neural networks to rainfall forecasting in Queensland, Australia

2. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

3. A new look at the statistical model identification

4. Individual and Combined Influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon

5. Influence of the Indian Ocean Dipole on the Australian winter rainfall

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