TY - JOUR
T1 - Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
AU - Hewage, Pradeep
AU - Behera, Ardhendu
AU - Trovati, Marcello
AU - Pereira, Ella
AU - Ghahremani, Morteza
AU - Palmieri, Francesco
AU - Liu, Yonghuai
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - Localized weather forecasting
KW - Long short-term memory (LSTM)
KW - Precision farming
KW - Temporal convolution networks (TCN)
KW - Time-series data analysis
UR - http://www.scopus.com/inward/record.url?scp=85084131649&partnerID=8YFLogxK
U2 - 10.1007/s00500-020-04954-0
DO - 10.1007/s00500-020-04954-0
M3 - Article
AN - SCOPUS:85084131649
SN - 1432-7643
VL - 24
SP - 16453
EP - 16482
JO - Soft Computing
JF - Soft Computing
IS - 21
ER -