Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

Adiqa Kausar Kiani, Wasim Ullah Khan, Muhammad Asif Zahoor Raja, Yigang He, Zulqurnain Sabir and Muhammad Shoaib
Additional contact information
Adiqa Kausar Kiani: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Wasim Ullah Khan: A School of Electrical Engineering and Automation, Wuhan University, East Lake South Road No. 8, Wuhan 430072, China
Muhammad Asif Zahoor Raja: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Yigang He: A School of Electrical Engineering and Automation, Wuhan University, East Lake South Road No. 8, Wuhan 430072, China
Zulqurnain Sabir: Department of Mathematics and Statistics, Hazara University, Mansehra 21120, Pakistan
Muhammad Shoaib: Department of Mathematics, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan

Sustainability, 2021, vol. 13, issue 17, 1-19

Abstract: The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10 ?9 to 10 ?10 and absolute error close to 10 ?5 to 10 ?7 . The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.

Keywords: environmental economic system; backpropagation networks; Bayesian regularization; Adams numerical solver; regression index (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/17/9537/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/17/9537/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:17:p:9537-:d:621166

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().