An Assessment of Machine Learning Integrated Autonomous Waste Detection and Sorting of Municipal Solid Waste

Abstract

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.

Photocatalytic degradation of drugs and dyes using a maching learning approach

“…[49][50][51] Furthermore, machine learning algorithms in waste management can also assist in decision-making processes for waste treatment and disposal. [52][53][54][55][56][57] Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and costeffectiveness. 25,[58][59][60][61][62] Because machine learning algorithms are suitable for depicting complex nonlinear processes, they are gradually being adopted to better manage waste and facilitate sustainable environmental development.…”

“…49–51 Furthermore, machine learning algorithms in waste management can also assist in decision-making processes for waste treatment and disposal. 52–57 Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and cost-effectiveness. 25,58–62…”

Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models

“…Learning Integrated Autonomous Waste Detection and Sorting of Municipal Solid Waste [13] Sonam Chaturvedi The application of back-to-back binary classifiers for the classification of organic-inorganic waste and further classification of organic waste is a suboptimal strategy to be deployed in real-world scenarios.…”

“…In [13], a smart mechanism for the prediction and forecasting of waste production is proposed. For the preprocessing and integration of data, CNN, along with an airjet structure, is used.…”