Deep Learning Based Effective Fine-grained Weather Forecasting Model


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108 Citations (Scopus)
152 Downloads (Pure)


It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compare its performance with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method, as well as the well-established weather research and forecasting (WRF) NWP model. More specifically Standard Regression (SR), Support Vector Regression (SVR), and Random Forest (RF) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA), Vector Auto Regression (VAR), and Vector Error Correction Model (VECM) are implemented as the statistical forecasting approaches. Furthermore, Arbitrage of Forecasting Expert (AFE) is implemented as the dynamic ensemble method in this article. Weather information is captured by time-series data and thus, we explore the state-of-art LSTM and TCN models, which is a specialised form of neural network for weather prediction. The proposed deep model consists of a number of layers that use surface weather parameters over a given period of time for weather forecasting. The proposed deep learning networks with LSTM and TCN layers are assessed in two different regressions, namely multi-input multi-output and multi-input single-output. 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 weather forecasting up to 12 h.

Original languageEnglish
Pages (from-to)343
JournalPattern Analysis and Applications
Early online date22 Jun 2020
Publication statusPublished - Feb 2021


  • Long Short-Term Memory
  • Temporal Convolutional Networks
  • Weather Prediction
  • Neural Network
  • WRF
  • Time-series Data Analysis
  • artificial intelligence
  • Regression model
  • Machine learning
  • Temporal convolutional networks
  • Weather prediction
  • Long short-term memory
  • Neural network
  • Time-series data analysis

Research Centres

  • Centre for Intelligent Visual Computing Research
  • Data Science STEM Research Centre
  • Data and Complex Systems Research Centre


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