Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models-Reference-Cited by-同舟云学术

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

Published:2020-07-07 Issue:11 Volume:65 Page:1928-1942
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Zhang Huihui¹²,Loáiciga Hugo A.²,Ren Fu¹³⁴,Du Qingyun¹³⁴^ORCID,Ha Da²⁵

Affiliation:

1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

2. Department of Geography, University of California, Santa Barbara, California, USA

3. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, China

4. Key Laboratory of Digital Mapping and Land Information Application Engineering, Ministry of Natural Resources, Wuhan University, Wuhan, China

5. School of Civil Engineering, Tianjin University, Tianjin, China

Funder

National Key R&D programme of China

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2020.1784901

Reference60 articles.

1. Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks

2. Application of Soft Computing and Intelligent Methods in Geophysics

3. Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

4. Study on the spatial differences and its time lag effect on climatic factors of the vegetation in the Longitudinal Range-Gorge Region

5. Long-Memory Processes

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