DCSNN optimized with hybrid Border Collie optimization and Archimedes optimization algorithms for solid waste prediction in Chennai

• Autorzy: Vidya Soundarapandian , Janani Elangeswaran Srie Vidhya , Sreedevi B.

Identyfikatory

DOI

10.37190/epe240101

• Języki publikacji: EN

Abstrakty

EN

The rapid growth of smart cities and industry causes an increase in waste production. The amount of municipal solid waste (MSW) increases by several factors, including population growth, economic status, and consumption trends. The inadequacy of basic trash data is a major issue for managing MSW. Numerous existing models based on solid waste prediction have been presented so far, but none of them predict solid waste accurately and also it consumes more time. To address these concerns, a deep convolutional spiking neural network for solid waste prediction (DCSNN-SWP) is proposed in this paper. Here, the real-time solid waste prediction data are gathered from the quantity of municipal corporation of Chennai (MCC), landfill, garden garbage, and coconut shell reports in Tamil Nadu (Chennai), such as Zone 9 (Nungambakkam), Zone 10 (Kodambakkam) and Zone 13 (Adyar). Then the collected solid waste data are pre-processed using the kernel correlation model. Then the pre-processing data is given to DCSNN-hybrid BCMO and Archimedes optimization algorithm which accurately predicts the solid waste as wet waste, dry waste, horticulture waste, and dumping yard for 2022-2032 years. The proposed DCSNN-SWP method has been implemented in Python.

Słowa kluczowe

EN

data handling; population statistics; economic status; neural networks; population growth; waste production; municipal solid waste

PL

przetwarzanie danych; statystyki ludności; status ekonomiczny; sieci neuronowe; wzrost populacji; produkcja odpadów; stałe odpady komunalne

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Environment Protection Engineering
• Rocznik: 2024
• Tom: Vol. 50, nr 1
• Strony: 5--25
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Vidya Soundarapandian

• Department of Computer Science Engineering, Sri Sairam Institute of Technology, Tamil Nadu, Chennai, India

• https://orcid.org/0000-0003-0962-8411

autor

Janani Elangeswaran Srie Vidhya

• Department of Computer Science and Engineering, Anna University Regional Campus, Tamil Nadu, Madurai, India

• https://orcid.org/0000-0003-3403-4949

autor

Sreedevi B.

• Department of Computer Science Engineering, Sri Sairam Institute of Technology, Tamil Nadu, Chennai, India

• https://orcid.org/0000-0003-0481-9913

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