Ambient Temperature Forecasting Using Non-linear Autoregressive Neural Networks Anas Kabbori

  • Anas Kabbori, Jilali Antari, Radouane Iqdour, Zine El Abidine El Morjani
Keywords: Forecasting, Non-linear Auto-Regressive (NAR), Non-linear Auto-Regressive with eXogenous input (NARX), modeling, natural hazards, Ambient Temperature.

Abstract

This paper aims to forecast ambient temperature values of Marrakesh City, using non-linear autoregressive neural networks. The process of forecasting ambient temperature, provides valuable data that improves prediction and prevention of natural disasters, especially heat waves and drought. Relying on Non-linear Auto-Regressive (NAR) neural networks and Non-linear Auto-Regressive with eXogenous input (NARX) neural networks on a dataset containing 12 years (2007-2019) of temperature data. The resulting values showed good performance using both networks. NARX networks showed better results in training and in testing phases. Those findings can be a stepping stone in agricultural seasons planning or modeling natural hazards and preventing them.

Published
2021-10-16
How to Cite
Radouane Iqdour, Zine El Abidine El Morjani, A. K. J. A. (2021). Ambient Temperature Forecasting Using Non-linear Autoregressive Neural Networks Anas Kabbori. Design Engineering, 4764-4781. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/5427
Section
Articles