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Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data

Abstract : Numerical Weather Prediction (NWP) requires considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a lightweight data-driven weather forecasting model by exploring state-of-the-art deep learning techniques based on Artificial Neural Network (ANN). Weather information is captured by time-series data and thus, we explore the latest Long Short-Term Memory (LSTM) layered model, which is a specialised form of Recurrent Neural Network (RNN) for weather prediction. The aim of this research is to develop and evaluate a short-term weather forecasting model using the LSTM and evaluate the accuracy compared to the well-established Weather Research and Forecasting (WRF) NWP model. The proposed deep model consists of stacked LSTM layers that uses surface weather parameters over a given period of time for weather forecasting. The model is experimented with different number of LSTM layers, optimisers, and learning rates and optimised for effective short-term weather predictions. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate short-term weather forecasting.
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Submitted on : Thursday, October 24, 2019 - 12:50:45 PM
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Pradeep Hewage, Ardhendu Behera, Marcello Trovati, Ella Pereira. Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.382-390, ⟨10.1007/978-3-030-19823-7_32⟩. ⟨hal-02331313⟩



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