Machine learning

Probabilistic forecasting

Convolutional neural network

Engineering

Artificial intelligence

Range (aeronautics)

Model output statistics

Global Forecast System

Weather forecasting

Aerospace engineering

Meteorology

Numerical weather prediction

Geography

Weather prediction

Probabilistic logic

Recurrent neural network

Computer science

Algorithm

Time series

Weather Research and Forecasting Model

North American Mesoscale Model

State (computer science)

Artificial neural network

Soft Computing

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.

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