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Article

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

by
Qingchun Guo
1,2,3,4,*,
Zhenfang He
1,2,
Zhaosheng Wang
5,
Shuaisen Qiao
1,
Jingshu Zhu
1 and
Jiaxin Chen
1
1
School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
2
Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
3
Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100081, China
4
State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
5
National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(19), 2870; https://doi.org/10.3390/w16192870
Submission received: 1 September 2024 / Revised: 5 October 2024 / Accepted: 8 October 2024 / Published: 9 October 2024
(This article belongs to the Section Water and Climate Change)
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Abstract

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.
Keywords: artificial intelligence; neural network; gate recurrent unit; long short-term memory; convolutional neural network; average atmospheric temperature; precipitation artificial intelligence; neural network; gate recurrent unit; long short-term memory; convolutional neural network; average atmospheric temperature; precipitation

Share and Cite

MDPI and ACS Style

Guo, Q.; He, Z.; Wang, Z.; Qiao, S.; Zhu, J.; Chen, J. A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models. Water 2024, 16, 2870. https://doi.org/10.3390/w16192870

AMA Style

Guo Q, He Z, Wang Z, Qiao S, Zhu J, Chen J. A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models. Water. 2024; 16(19):2870. https://doi.org/10.3390/w16192870

Chicago/Turabian Style

Guo, Qingchun, Zhenfang He, Zhaosheng Wang, Shuaisen Qiao, Jingshu Zhu, and Jiaxin Chen. 2024. "A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models" Water 16, no. 19: 2870. https://doi.org/10.3390/w16192870

APA Style

Guo, Q., He, Z., Wang, Z., Qiao, S., Zhu, J., & Chen, J. (2024). A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models. Water, 16(19), 2870. https://doi.org/10.3390/w16192870

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