DRN-DSA: A hybrid deep learning network model for precipitation nowcasting using time series data
Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric conditions, it aids meteorologists in identifying weather patterns and preparing for severe events such as flooding. These nowcasts are typically disp...
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| Published in: | Knowledge-based systems Vol. 306; p. 112679 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
20.12.2024
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| ISSN: | 0950-7051 |
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| Abstract | Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric conditions, it aids meteorologists in identifying weather patterns and preparing for severe events such as flooding. These nowcasts are typically displayed on geographical maps by weather services. However, the rapidly changing climate conditions make precipitation nowcasting a formidable challenge, as accurate short-term forecasts are hindered by immediate weather fluctuations. Traditional nowcasting methods, like numerical models and radar extrapolation, have limitations in delivering highly detailed and timely precipitation nowcasts. To overcome this issue, an effective solution is framed for precipitation nowcasting using a hybrid network approach named Deep Residual Network-Deep Stacked Autoencoder (DRN-DSA). Initially, the input time series data is acquired from the dataset. Thereafter, the effective technical indicators are extracted at the feature extraction stage. Later on, precipitation-type nowcasting is carried out using the proposed hybrid DRN-DSA, which is developed by incorporating a Deep Stacked Autoencoder (DSA) and Deep Residual Network (DRN). Finally, Weather nowcasting is carried out using the same proposed hybrid DSA-DRN. Moreover, when compared to other traditional models, the proposed DRN-DSA has gained superior results with a Relative Absolute Error (RAE) of 0.295, Root Mean Square Error (RMSE) of 0.154, low Mean Square Error (MSE) of 0.0236, Mean Absolute Percentage Error (MAPE) of 0.295, and False Acceptance Rate (FAR) of 0.0118. |
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| AbstractList | Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric conditions, it aids meteorologists in identifying weather patterns and preparing for severe events such as flooding. These nowcasts are typically displayed on geographical maps by weather services. However, the rapidly changing climate conditions make precipitation nowcasting a formidable challenge, as accurate short-term forecasts are hindered by immediate weather fluctuations. Traditional nowcasting methods, like numerical models and radar extrapolation, have limitations in delivering highly detailed and timely precipitation nowcasts. To overcome this issue, an effective solution is framed for precipitation nowcasting using a hybrid network approach named Deep Residual Network-Deep Stacked Autoencoder (DRN-DSA). Initially, the input time series data is acquired from the dataset. Thereafter, the effective technical indicators are extracted at the feature extraction stage. Later on, precipitation-type nowcasting is carried out using the proposed hybrid DRN-DSA, which is developed by incorporating a Deep Stacked Autoencoder (DSA) and Deep Residual Network (DRN). Finally, Weather nowcasting is carried out using the same proposed hybrid DSA-DRN. Moreover, when compared to other traditional models, the proposed DRN-DSA has gained superior results with a Relative Absolute Error (RAE) of 0.295, Root Mean Square Error (RMSE) of 0.154, low Mean Square Error (MSE) of 0.0236, Mean Absolute Percentage Error (MAPE) of 0.295, and False Acceptance Rate (FAR) of 0.0118. |
| ArticleNumber | 112679 |
| Author | Rudrappa, Gujanatti Vijapur, Nataraj |
| Author_xml | – sequence: 1 givenname: Gujanatti orcidid: 0009-0003-0743-7520 surname: Rudrappa fullname: Rudrappa, Gujanatti email: rudraguj@gmail.com organization: Assistant Professor, Department of Electronics and Communication Engineering, KLE Technological University Dr. M S Sheshgiri Campus, Belagavi, Research Centre: Department of Electronics and Communication Engineering, RV Institute of Technology and Management, Bangalore, Visvesvaraya Technological University, Belagavi, Karnataka, 590018, India – sequence: 2 givenname: Nataraj surname: Vijapur fullname: Vijapur, Nataraj email: nvijapur@gmail.com organization: Associate Professor, Department of Electronics and Communication Engineering, RV Institute of Technology and Management, Bangalore, Visvesvaraya Technological University, Belgavi, Karantaka 590018, India |
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| Cites_doi | 10.1016/j.enconman.2019.111793 10.3390/rs13214285 10.1016/j.engappai.2010.09.007 10.1002/ett.4640 10.1038/s41586-021-03854-z 10.1016/j.jag.2024.103962 10.3390/w14162570 10.1016/j.patrec.2021.01.036 10.1016/j.neucom.2021.02.072 10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2 10.3390/rs11192303 10.3390/axioms11030107 10.1016/j.bspc.2024.106177 10.1016/j.procs.2019.02.036 10.1016/j.dibe.2023.100128 10.3390/rs15010142 10.1080/15481603.2023.2203363 10.1109/JSEN.2018.2831082 |
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| Keywords | Deep Residual Network (DRN) time series data Deep Learning (DL) Precipitation Nowcasting Deep Stacked Autoencoder (DSA) |
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| Snippet | Precipitation nowcasting involves short-term weather forecasting, predicting rain or snow within the next two hours. By analyzing current atmospheric... |
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| SubjectTerms | Deep Learning (DL) Deep Residual Network (DRN) Deep Stacked Autoencoder (DSA) Precipitation Nowcasting time series data |
| Title | DRN-DSA: A hybrid deep learning network model for precipitation nowcasting using time series data |
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