Current Research Bulletin

Weather forecasting remains an important scientific activity that is intricately connected to human life activities like agriculture, transportation, disaster management, and even public health. Predictive accuracy improves the ability of society to cope with extreme weather conditions, increases agricultural outputs, and reduces the risks because of climate change. Although there have been advancements in meteorological science, the chaotic and non-linear nature of atmospheric processes makes precise forecasting still a complicated challenge. There is always a combination of statistical and numerical weather prediction models, which is greatly inefficient when it comes to capturing long-range dependencies and complex temporal patterns. The results are poor with mid- to long-range forecasts. With the increase in concern over climate change and availability of deep learning algorithms, there is an opportunity to use modern machine learning techniques to improve predictive accuracy. Weather forecasting has time-series data, and hence deep learning models such as Time-Aware Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and even the newer Transformer-based architectures are suitable. Based on the comparison of models’ performance, it can be concluded that the GRU model is the best among all with the highest prediction accuracy considering all parameters—Mean Square Error (MSE), Mean Absolute Error (MAE), and R-square values.

Temperature, Weather, Gated Recurrent Units, Predictions, Long Short-Term Memory

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