Open AccessAn Assessment of Machine Learning Integrated Autonomous Waste Detection and Sorting of Municipal Solid Waste
Author(s) -
Sonam Chaturvedi,
Bikarama Prasad Yadav,
Nihal Anwar Siddiqui
Publication year - 2021
Publication title -
nature, environment and pollution technology/nature, environment and pollution technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.154
H-Index - 11
eISSN - 2395-3454
pISSN - 0972-6268
DOI - 10.46488/nept.2021.v20i04.013
Subject(s) - municipal solid waste , artificial neural network , metropolitan area , sorting , engineering , variety (cybernetics) , waste management , computer science , environmental science , machine learning , artificial intelligence , medicine , pathology , programming language
Municipal solid waste deposition in metropolitan areas has become a major concern that, if not addressed, can lead to environmental degradation and possibly endanger human health. It is important to adopt a smart waste management system in place to cope with a range of waste materials. This research aims to develop a smart modelling method that could accurately predict and forecast the production of municipal solid waste. An integrated convolution neural network and air-jet system-based framework developed for pre-processing and data integration were developed. The results showed that machine learning algorithms could be used to detect different types of waste with high accuracy. The best performers were obtained from neural network models, which captured 72% of the information variation. The method proposed in this study demonstrates the feasibility of developing tools to assist urban waste through the supply, pre-processing, integration, and modelling of data accessible to the public from a variety of sources.