Sustainable Waste Management Using Deep Learning and Smart Bins (Published)
Waste Sorting as a Service is an innovative deep learning-based waste management system smart technology for advanced waste classification. The system’s core consists of a Convolutional Neural Network (CNN) where a voluminous image database is trained to sort and identify waste, where wastes are identified under different classifications including organic, plastics, glass, and paper. Once the system recognizes through image recognition the type of waste it labels according to the context, the motors powered by Arduino open the proper bin compartment for sorting in real-time. Where mixed or incorrectly classified waste is found, the system forbids the opening of any bin to allow users to dispose of the waste correctly. Each part of the smart bins is designed to accommodate particular sorts of waste, which lessens the intermingling of waste sorts and raises the recycling rate. The combination of CNN-based image recognition applied to bin identification with the automatic control of the bins, on the other hand, is not only efficient and convenient in handling the waste disposal system but also beneficial in helping the cause of environmentalism through a decrease in the volume of waste ending up in the landfills as well as encouraging everyone and anybody into adopting the correct ways of recycling. The design of the system allows the extension to both urban environments and concepts of smart cities, the solutions for granting sustainable waste management and at the same time using up-to-date automation and deep learning technologies in real-time.
Keywords: Arduino-controlled smart bin, Live classification, Waste Management, deep learning
AN EVALUATION OF THE OPERATIONAL EFFICIENCY OF A PUBLIC AGENCY: A CASE STUDY OF ENUGU STATE WASTE MANAGEMENT AUTHORITY (ESWAMA) IN ENUGU CITY, NIGERIA (Published)
Enugu State Waste Management Authority (ESWAMA) is a public agency established in 2004 with the mandate for waste management in the urban areas in Enugu State including Enugu City. After 9 years of existence, it becomes necessary to evaluate its operational efficiency for the purpose of identifying its worth, strength and challenges. For this evaluation, 79 households served as respondents and were purposively selected from the three Local government areas that make up Enugu City. The questionnaire instrument used for data collection contained the 18 statutory functions of ESWAMA and respondents were requested to rate each function as follows; very good (VG), Good (G), Fair (F), Poor (P) and Unknown (U) depending on their perception. Relative Satisfaction Indices (RSIs) were computed for the 79 respondents across the 18 functions in keeping with Likert weighting scale. The results were combined with the outcome of the structured interviews and reasons adduced by the respondents. In accordance with Likert scale, 3 classes of efficiency were established for all the 18 functions. Result showed that ESWAMA scored pass mark in only 22.2% of its functions, and fair mark in another 22.2% and poor grade in 55. 6% of its functions. ESWAMA operational efficiency is therefore found to be very poor, partial and narrow in scope in relation to its entire statutory functions. Responsive leadership with good training, skill and knowledge in environmental sciences and management is recommended to improve its operational level of efficiency.
Keywords: ESWAMA, Evaluation, Operational Efficiency, Statutory Function., Waste Management