Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model
Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is one of the important indicators in detecting clim...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9849651/ |
| _version_ | 1818059047295778816 |
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| author | Masooma Ali Raza Suleman S. Shridevi |
| author_facet | Masooma Ali Raza Suleman S. Shridevi |
| author_sort | Masooma Ali Raza Suleman |
| collection | DOAJ |
| description | Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short-Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset. |
| first_indexed | 2024-12-10T13:10:19Z |
| format | Article |
| id | doaj.art-0c5bee1749e540098b13b64e32ebc265 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T13:10:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0c5bee1749e540098b13b64e32ebc2652022-12-22T01:47:42ZengIEEEIEEE Access2169-35362022-01-0110824568246810.1109/ACCESS.2022.31963819849651Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM ModelMasooma Ali Raza Suleman0https://orcid.org/0000-0003-1883-2158S. Shridevi1https://orcid.org/0000-0002-0038-7212School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaCentre for Advanced Data Science, Vellore Institute of Technology, Chennai, IndiaWeather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short-Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset.https://ieeexplore.ieee.org/document/9849651/Sustainable environmental developmentweather forecastingrecurrent neural network (RNN)long short-term memory (LSTM)spatial feature |
| spellingShingle | Masooma Ali Raza Suleman S. Shridevi Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model IEEE Access Sustainable environmental development weather forecasting recurrent neural network (RNN) long short-term memory (LSTM) spatial feature |
| title | Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model |
| title_full | Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model |
| title_fullStr | Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model |
| title_full_unstemmed | Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model |
| title_short | Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model |
| title_sort | short term weather forecasting using spatial feature attention based lstm model |
| topic | Sustainable environmental development weather forecasting recurrent neural network (RNN) long short-term memory (LSTM) spatial feature |
| url | https://ieeexplore.ieee.org/document/9849651/ |
| work_keys_str_mv | AT masoomaalirazasuleman shorttermweatherforecastingusingspatialfeatureattentionbasedlstmmodel AT sshridevi shorttermweatherforecastingusingspatialfeatureattentionbasedlstmmodel |