Open Access

ARTICLE

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

by Malini M. Patil^1,*, P. M. Rekha¹, Arun Solanki², Anand Nayyar^3,4, Basit Qureshi⁵

1 JSS Academy of Technical Education, Bengaluru, 560060, India
2 Gautam Buddha University, Greater Noida, 201312, India
3 Graduate School, Duy Tan University, Da Nang, 550000, Vietnam
4 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam
5 College of Computer & Info Sc, Prince Sultan University, Saudi Arabia

* Corresponding Author: Malini M. Patil. Email:

Computers, Materials & Continua 2022, 71(2), 2347-2361. https://doi.org/10.32604/cmc.2022.021447

Received 03 July 2021; Accepted 27 September 2021; Issue published 07 December 2021

Abstract

In the past few decades, climatic changes led by environmental pollution, the emittance of greenhouse gases, and the emergence of brown energy utilization have led to global warming. Global warming increases the Earth's temperature, thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people. Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer, causing the untimely death of thousands of people. To forecast weather conditions, researchers have utilized machine learning algorithms, such as autoregressive integrated moving average, ensemble learning, and long short-term memory network. These techniques have been widely used for the prediction of temperature. In this paper, we present a swarm-based approach called Cauchy particle swarm optimization (CPSO) to find the hyperparameters of the long short-term memory (LSTM) network. The hyperparameters were determined by minimizing the LSTM validation mean square error rate. The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City. The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset. The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output. The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training. The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error (RMSE) value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

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