Municipal Solid Waste Prediction using Tree Hierarchical Deep Convolutional Neural Network Optimized with Balancing Composite Motion Optimization Algorithm

Abstract

ABSTRACTEfficacious forecasting of a solid waste supervision system depends on the prediction accuracy of solid waste generation. Several existing methods on municipal solid waste prediction were suggested previously, but those methods do not accurately predict the solid waste, and also it takes high computation time. To overwhelm these issues, Municipal Solid Waste Prediction using Tree Hierarchical Deep Convolutional Neural Network optimised with Balancing Composite Motion Optimization algorithm (MSWP-THDCNN-BCMOA) is proposed for municipal solid waste prediction. Initially, real-time solid waste prediction data is taken from Quantity of MCC, Landfill, Gardan Garbage and Coconut Shell Report 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 morphological filtering and extended empirical wavelet transformation. Then the pre-processed data are given to THDCNN-BCMOA algorithm, which accurately predicts the solid waste as wet waste, dry waste, horticulture waste, and dumping yard for 2025–2035 years. The proposed MSWP-THDCNN-BCMOA method is implemented in Python. Then the proposed MSWP-THDCNN-BCMOA method attains 17.91%, 28.30%, 5.63% and 13.54% higher accuracy, 98.66%, 99.13%, 96.43% and 98.31% lower error rate, 53.003%, 48.44%, 25.69% and 42.42% lower computation time compared with existing methods.KEYWORDS: Morphological filtering and extended empirical wavelet transformationtree hierarchical deep convolutional neural networkbalancing composite motion optimizationmunicipal solid waste prediction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.