Journal
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
Volume 149, Issue -, Pages 399-409Publisher
ELSEVIER
DOI: 10.1016/j.psep.2020.11.007
Keywords
COVID-19; Optimization; Chaotic marine predators algorithm; Forecasting; Artificial intelligence; Russia; Brazil
Categories
Funding
- Hubei Provincinal Science and Technology Major Project of China [2020AEA011]
- Key Research & Developement Plan of Hubei Province of China [2020BAB100]
Ask authors/readers for more resources
COVID-19, a new member of the Coronaviridae family, has been declared a global pandemic by WHO. This paper proposes a new short-term forecasting model with CMPA performing significantly better than other investigated models.
COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gas-trointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and par-ticle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.(c) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available