Prediction of Weather Forecasting with Long Short-Term Memory using Deep Learning

Weather forecasting has a greater effect on human life, recently this filed has gained a significant research attention. The existing numerical weather prediction models are very complex to solve. Therefore, the primary goal of this research work is to create a novel and lightweight weather forecast...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1161 - 1168
Main Authors S, Karthika, Priyanka, T, Indirapriyadharshini, J., Sadesh, S, G, Rajeshkumar, P, Rajesh Kanna
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Deep learning
Long Short-Term Memory
LSTM
Numerical models
Prediction algorithms
Predictive models
Time series analysis
Weather forecasting
Weather prediction
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Summary:Weather forecasting has a greater effect on human life, recently this filed has gained a significant research attention. The existing numerical weather prediction models are very complex to solve. Therefore, the primary goal of this research work is to create a novel and lightweight weather forecasting model. Here, the time series data have been used from Kaggle website. The obtained dataset is then processed with the help of deep learning techniques. The Long Short-Term Memory (LSTM) algorithm is used to produce better result with more accuracy when compared with other deep learning methodologies. Therefore, from the results it is evident that the proposed model can be used to predict the weather conditions with greater accuracy and assist farmers and others who depend on weather.
DOI:10.1109/ICOSEC58147.2023.10276273