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

Research output: Contribution to journalConference proceeding article (ISSN)peer-review

9 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)382-390
Number of pages9
JournalIFIP Advances in Information and Communication Technology
Publication statusPublished - 12 May 2019


  • Long Short-Term Memory
  • Numerical Weather Prediction
  • WRF
  • Surface Weather Parameters
  • time-series data analysis


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