Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model
نویسندگان
چکیده
Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back progress in by years. The change surface temperature is as one of important indicators detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short Term Memory (SFA-LSTM) capture accurate spatial temporal relations multiple features forecast temperature. Significant feature interpretations historical data aligned directly output helps accurately. attention captures mutual influence input on target feature. built using encoder-decoder architecture, where dependencies are learnt LSTM layers encoder phase decoder phase. SFA-LSTM forecasts simultaneously most time steps weather variables. When compared with baseline models, maintains state-of the-art accuracy while offering benefit appropriate interpretability. learned weights validated magnitude correlation obtained dataset.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3196381