干旱区生态环境知识资源中心(Arid): RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China

Arid

DOI	10.3390/w15203650
	RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China
	Liu, Yulong; Liu, Shuxian; Chen, Juepu
通讯作者	Liu, SX
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:20
英文摘要	Accurate precipitation forecasting is of great significance to social life and economic activities. Due to the influence of various factors such as topography, climate, and altitude, the precipitation in semi-arid and arid areas shows the characteristics of large fluctuation, short duration, and low probability of occurrence. Therefore, it is highly challenging to accurately predict precipitation in the northern Xinjiang area of China, which is located in the semi-arid and arid climate region. In this study, six meteorological stations in the northern Xinjiang area were selected as the research area. Due to the high volatility of rainfall in this area, the rainfall was divided into four levels, namely, no rain, light rain, moderate rain, and heavy rain and above, for rainfall level prediction. In order to improve the prediction performance, this study proposed a rainfall levels nowcasting model based on Conv1D_Transformer (RLNformer). Firstly, the maximum information coefficient (MIC) method was used for feature selection and sliding the data, that is, the data of the first 24 h were used to predict the rainfall levels in the next 3 h. Then, the Conv1D layer was used to replace the word-embedding layer of the transformer, enabling it to extract the relationships between features of time series data and allowing multi-head attention to better capture contextual information in the input sequence. Additionally, a normalization layer was placed before the multi-head attention layer to ensure that the input data had an appropriate scale and normalization, thereby reducing the sensitivity of the model to the distribution of input data and helping to improve model performance. To verify the effectiveness and generalization of the proposed model, the same experiments were conducted on the Indian public dataset, and seven models were selected as benchmark models. Compared with the benchmark models, RLNformer achieved the highest accuracy on both datasets, which were 96.41% and 88.95%, respectively. It also had higher accuracy in the prediction of each category, especially the minority category, which has certain reference significance and practical value.
英文关键词	time series data rainfall levels nowcasting Conv1D transformer
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001089489400001
WOS关键词	LEARNING APPROACH
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399090
推荐引用方式 GB/T 7714	Liu, Yulong,Liu, Shuxian,Chen, Juepu. RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China[J],2023,15(20).
APA	Liu, Yulong,Liu, Shuxian,&Chen, Juepu.(2023).RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China.WATER,15(20).
MLA	Liu, Yulong,et al."RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China".WATER 15.20(2023).