Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide th...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2020-11, Vol.24 (21), p.16453-16482
Main Authors: Hewage, Pradeep, Behera, Ardhendu, Trovati, Marcello, Pereira, Ella, Ghahremani, Morteza, Palmieri, Francesco, Liu, Yonghuai
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Computational Intelligence
Control
Deep learning
Engineering
Farmers
Fuzzy logic
Machine learning
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Methodologies and Application
Neural networks
Numerical weather forecasting
Personal computers
Precipitation
Prediction models
Radiation
Regional development
Robotics
Time series
Weather forecasting
Weather stations
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Summary:Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-04954-0