Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local w
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METHODOLOGIES AND APPLICATION
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station Pradeep Hewage1 • Ardhendu Behera1 • Marcello Trovati1 • Ella Pereira1 • Morteza Ghahremani2 Francesco Palmieri3 • Yonghuai Liu1
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The Author(s) 2020
Abstract 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. Keywords Localized weather forecasting Time-series data analysis Temporal convolution networks (TCN) Long short-term memory (LSTM) Precision farming
1 Introduction Non-predictive or inaccurate weather forecasting can severely impact the community of users. For example, farmers depend on the weather forecast so that various farming activities can be undertaken such as ploughing, cultivation, harvesting, and others. An inaccurate forecast directly impacts the farmer’s ability to engage these activities, influencing their capability of managing the resources related to such operations (Ho et al. 2012). In
Communicated by V. Loia. & Pradeep Hewage [email protected] 1
Department of Computer Science, Edge Hill University, Ormskirk, Lancashire L39 4QP, UK
2
Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK
3
Department of Computer Science, Universita degili Studi di Salerno, Fisciano, Italy
addition, there are significant risks to life and property loss due to unexpected weather conditions all over the world (Fente and Singh 2018). Furthermore, the regional weather forecast may not be accurate based on the geographical appearance of the location, such as but not limited to the top of a mountain, land covered by several mountains, and the slope of the land (Mass and Kuo 1998). Therefore, accurate localized weather prediction system wo
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