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Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

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Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China
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School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Key Laboratory of Meteorological Disaster of Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Institute for Climate and Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
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Author to whom correspondence should be addressed.
Academic Editor: Aizhong Ye
Water 2021, 13(22), 3294; https://doi.org/10.3390/w13223294
Received: 22 October 2021 / Revised: 14 November 2021 / Accepted: 17 November 2021 / Published: 21 November 2021
The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions. View Full-Text
Keywords: Yangtze River valley; seasonal prediction; random forest; machine learning Yangtze River valley; seasonal prediction; random forest; machine learning
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MDPI and ACS Style

He, C.; Wei, J.; Song, Y.; Luo, J.-J. Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions. Water 2021, 13, 3294. https://doi.org/10.3390/w13223294

AMA Style

He C, Wei J, Song Y, Luo J-J. Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions. Water. 2021; 13(22):3294. https://doi.org/10.3390/w13223294

Chicago/Turabian Style

He, Chentao, Jiangfeng Wei, Yuanyuan Song, and Jing-Jia Luo. 2021. "Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions" Water 13, no. 22: 3294. https://doi.org/10.3390/w13223294

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