Improving earth surface temperature forecasting through the optimization of deep learning hyper-parameters using barnacles mating optimizer

Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Muhammad 'Arif, Mohamad (2024) Improving earth surface temperature forecasting through the optimization of deep learning hyper-parameters using barnacles mating optimizer. Franklin Open, 8 (100137). pp. 1-10. ISSN 2773-1863. (Published)

Preview

Pdf
Improving earth surface temperature forecasting.pdf
Available under License Creative Commons Attribution.
Download (3MB) | Preview

DOI/Official URL: https://doi.org/10.1016/j.fraope.2024.100137

Abstract

Time series forecasting is crucial across various sectors, aiding stakeholders in making informed decisions, planning for the short and long term, managing risks, optimizing profits, and ensuring safety. One significant application of time series forecasting is predicting Earth surface temperatures, which is vital for civil and environmental sectors such as agriculture, energy, and meteorology. This study proposes a hybrid forecasting model for Earth surface temperature using Deep Learning (DL). To improve the DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) is integrated to optimize both weights and biases. The forecasting model is trained on a global temperature dataset with seven inputs and compared with DL models optimized by Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA), and Ant Colony Optimization (ACO). Additionally, a comparison is made with the Autoregressive Moving Average (ARIMA) method. Evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2) demonstrates the superior performance of DL optimized by BMO, showing minimal errors.

Item Type:	Article
Uncontrolled Keywords:	Barnacles Mating Optimizer; Deep learning; Optimization; Time series prediction; Earth Surface Temperature; Climate change
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Computing Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	14 Aug 2024 03:38
Last Modified:	14 Aug 2024 03:38
URI:	http://umpir.ump.edu.my/id/eprint/42346
Download Statistic:	View Download Statistics

Actions (login required)

View Item