Abstract
Weather prediction task remains a challenging problem in computer vision field although some solutions have been used for many applications such as air/sea transportation. The increasing requirement toward safer human transportation that requires a robust weather prediction model has motivated the development of a vast number of weather prediction models. In the past decade, the advent of deep learning methods has opened up a new approach to weather prediction, mainly in two areas: automated learning hierarchical representation of weather data and robust weather prediction models. This paper presents a method for automatic feature extraction from weather time series data using Autoencoder model. The learned weather representation was used to train Long Short-term Memory model as a prediction model or regressor. Although it can be used to predict many other weather variables, in this study, the proposed model was tested to predict temperature, dew point, and humidity. The results show that the model performance measured by training and testing RMSE values are as follows. Predicting temperature: AE90-LSTM model (0.00003, 0.00010) and predicting dew point: AE199-LSTM model (0.00005, 0.00010). Interestingly, for predicting humidity 100LSTM model (0.00004, 0.00001) and AE100-LSTM model (0.00001, 0.00008) achieved almost similar performance.
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Heryadi, Y. (2019). Learning Hierarchical Weather Data Representation for Short-Term Weather Forecasting Using Autoencoder and Long Short-Term Memory Models. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_32
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