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

By Pradeep Ruwan Padmasiri Galbokka Hewage, Ardhendu Behera, M. Trovati, E. Pereira, Morteza Ghahremani, F. Palmieri, Yonghuai Liu. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2020

657 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/c82bd192018d37dd3b58a681e0b774201d7a8a16

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In Artificial Intelligence

1. Haixu Wu
2. Xiaoming Shi
3. Huakun Luo
4. Shiyu Wang

In Electrical and Electronic Engineering

1. Haixu Wu

Citations in the corpus

• TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting (2024, methodology): “…variations with well-designed architectures, which span a wide range of foundation backbones, including CNN (Wang et al., 2023; Wu et al., 2023a; Hewage et al., 2020), RNN (Lai et al., 2018; Qin et al., 2017; Salinas et al., 2020), Transformer (Vaswani et al., 2017; Zhou et al., 2021; Wu et…”