Study On Hydrological Forecasting By Mean Generating Function And Random Forest Method-Reference-Cited by-同舟云学术

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Study On Hydrological Forecasting By Mean Generating Function And Random Forest Method

Published:2024-05-17 Issue: Volume:42 Page:259-262
ISSN:
Container-title:Proceedings of the 2024 International Conference on Smart City and Information System
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Author:

Zhang Wenming¹^ORCID,Du Yong¹^ORCID,Lv Meng²^ORCID

Affiliation:

1. Bureau of Hydrology and Water Resources, Pearl River Water Resources Commission of Ministry of Water Resources, China

2. College of Hydrology and Water Resources, Hohai University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3685088.3685135

Reference12 articles.

1. Du Liangmin, Ke Zongjian, Liu Changzheng, A Model for Predicting Summer Precipitation in China Based on Cluster Partitioning [J]. Meteorology, 2016, 42 (01): 89-96.

2. He Hui, Lu Hong, Qin Weijian, Review of Research and Application of Artificial Neural Networks in Monthly Precipitation Prediction Business [J]. Meteorological Research and Application, 2021, 42 (01): 1-6.

3. Machine learning based medium- and long-term runoff prediction of the Yangtze River main stream [J];Wenjing Xiao;Journal of Hydroelectric Energy Science,2022

4. Jin Jian, Liu Yu, Wang Qinhui, Medium and Long Term Runoff Forecast Based on Wavelet Denoising and FA-SVM [J]. People's Yangtze River, 2020, 51 (09): 67-72.

5. Application of stepwise regression model of mean-generating function in long-term hydrological forecasting[J];Jian Feng;Northeast Water Resources and Hydropower,2010

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