RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/20/3650 |
| _version_ | 1797572035398860800 |
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| author | Yulong Liu Shuxian Liu Juepu Chen |
| author_facet | Yulong Liu Shuxian Liu Juepu Chen |
| author_sort | Yulong Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:48:56Z |
| format | Article |
| id | doaj.art-81880d067d3f45b49cec6b3ac16cf1f7 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T20:48:56Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-81880d067d3f45b49cec6b3ac16cf1f72023-11-19T18:30:51ZengMDPI AGWater2073-44412023-10-011520365010.3390/w15203650RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of ChinaYulong Liu0Shuxian Liu1Juepu Chen2School of Computer Science and Technology, Xinjiang University, Urumqi 830017, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830017, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi 830017, ChinaAccurate 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.https://www.mdpi.com/2073-4441/15/20/3650time series datarainfall levels nowcastingConv1Dtransformer |
| spellingShingle | Yulong Liu Shuxian Liu Juepu Chen RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China Water time series data rainfall levels nowcasting Conv1D transformer |
| title | RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China |
| title_full | RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China |
| title_fullStr | RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China |
| title_full_unstemmed | RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China |
| title_short | RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China |
| title_sort | rlnformer a rainfall levels nowcasting model based on conv1d transformer for the northern xinjiang area of china |
| topic | time series data rainfall levels nowcasting Conv1D transformer |
| url | https://www.mdpi.com/2073-4441/15/20/3650 |
| work_keys_str_mv | AT yulongliu rlnformerarainfalllevelsnowcastingmodelbasedonconv1dtransformerforthenorthernxinjiangareaofchina AT shuxianliu rlnformerarainfalllevelsnowcastingmodelbasedonconv1dtransformerforthenorthernxinjiangareaofchina AT juepuchen rlnformerarainfalllevelsnowcastingmodelbasedonconv1dtransformerforthenorthernxinjiangareaofchina |