气候变化领域集成服务门户(CCPortal): A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

CCPortal

DOI	10.3390/w16101423
	A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network
	Wang, Bing-Zeng; Liu, Si-Jie; Zeng, Xin-Min; Lu, Bo; Zhang, Zeng-Xin; Zhu, Jian; Ullah, Irfan
发表日期	2024
EISSN	2073-4441
起始页码	16
结束页码	10
卷号	16 期号:10
英文摘要	In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change.
英文关键词	pre-summer rainy season; precipitation forecasting; back-propagation neural network; triangular irregular network
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
WOS类目	Environmental Sciences ; Water Resources
WOS记录号	WOS:001231579000001
来源期刊	WATER
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291104
作者单位	Hohai University; Hohai University; China Meteorological Administration
推荐引用方式 GB/T 7714	Wang, Bing-Zeng,Liu, Si-Jie,Zeng, Xin-Min,et al. A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network[J],2024,16(10).
APA	Wang, Bing-Zeng.,Liu, Si-Jie.,Zeng, Xin-Min.,Lu, Bo.,Zhang, Zeng-Xin.,...&Ullah, Irfan.(2024).A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network.WATER,16(10).
MLA	Wang, Bing-Zeng,et al."A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network".WATER 16.10(2024).