Abstract: In recent years, the field of short-term predictions has witnessed substantial advancements due to the rapid growth in data-driven approaches. To contribute to this area of research, a novel model named the Spatial Feature Attention-based LSTM (Long Short-Term Memory) has been developed, aiming to enhance the accuracy and reliability of short-term predictions. The advent of deep learning techniques has revolutionized various domains and one such domain where these techniques have made significant strides in time series forecasting. Weather forecasting is crucial for various industries, including agriculture, transportation, and disaster management. The accuracy of short-term weather predictions significantly impacts decision-making and planning.

Keywords: LSTM Model, Short Term Prediction, Spatial Feature, Weather Forecasting.

Prof. Nilam Honmane, Omkar Jadhav, Aditya Gavate, Sanika Nimse, Roshan Jadhav, "Weather Forecasting Using Spatial Feature Based LSTM Model", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 12, pp. 73-78, 2023, Crossref https://doi.org/10.17148/IJARCCE.2023.121212.

PDF | DOI: 10.17148/IJARCCE.2023.121212

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