Deep learning-based effective fine-grained weather forecasting model

Abstract

It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compare its performance with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method, as well as the well-established weather research and forecasting (WRF) NWP model. More specifically Standard Regression (SR), Support Vector Regression (SVR), and Random Forest (RF) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA), Vector Auto Regression (VAR), and Vector Error Correction Model (VECM) are implemented as the statistical forecasting approaches. Furthermore, Arbitrage of Forecasting Expert (AFE) is implemented as the dynamic ensemble method in this article. Weather information is captured by time-series data and thus, we explore the state-of-art LSTM and TCN models, which is a specialised form of neural network for weather prediction. The proposed deep model consists of a number of layers that use surface weather parameters over a given period of time for weather forecasting. The proposed deep learning networks with LSTM and TCN layers are assessed in two different regressions, namely multi-input multi-output and multi-input single-output. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate weather forecasting up to 12 h.

Retracted: Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

“…This deep learning network with TCN and LSTM layers were evaluated in two distinct regressions, in particular multi-input single-output and multi-input multi-output. The weather prediction results predicted up to 12 h. 9 Liu et al proposed a computational intelligence technique called stacked auto-encoder for simulating hourly weather information in 30 years. This technique could learn the features automatically from the large amount of dataset by means of feature granulation layer-by-layer, and the huge size of the dataset could ensure that the complicated deep model does preventing from the overfitting issue.…”

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

“…It has been verified as the highly professional probabilistic classifier that has solid mathematical fundamentals (Kumar et al 2019;Rabie et al 2015Rabie et al , 2019a. NB has worked very well in several complex real-world applications such as; medical diagnosis, real-time prediction, spam filtering, and weather forecasting despite its oversimplified assumptions and its Naïve design (Dada et al 2019;Ali and Ali 2020;Hewage et al 2020;Lei et al 2020). Thus, NB can be considered as one of the best classifiers that can be applied for COVID-19 detection.…”

A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

“…Therefore, the prediction of learning supported rain diminution is standard because the problem of the NWP technique can be solved. In [ 57 , 58 , 59 , 60 , 61 , 62 , 63 ] ML-based rainfall prediction techniques were presented. Table 4 lists some of the error estimation techniques for rain rate prediction.…”