Raj Kumar Sharma
ABSTRACT
The environment faces significant issues from garbage management, which is quite essential. It is a crucial issue for Municipal Corporation organisations. Different techniques were periodically developed to evaluate the effectiveness of the garbage management system. Cities are getting more significant as technology advances. As a result, more effective management and engineering tools have been developed. This research paper focuses on the municipal dataset provided by the garbage management systems of 26 Indian cities. The data have been interpreted, analysed, and summarised using descriptive and inferential statistics methods. The statistical technique has evaluated a potential linear link between variables. The Ordinary Least Squares regression model has been applied to a distinct set of data variables based on correlations. The alternative hypothesis that corresponds to the null hypothesis is created. The regression models' coefficient of determination is calculated and discovered to be about 90%. The outcomes of the t-test and f-test analyses demonstrate the significance of the independent variables and the overall model. The R2 values between dependent and independent variables (Garbage & Population), (House & Population), (Garbage & House), (Area & Garbage), (Vehicle & Garbage), and (Worker & Garbage) have been found to 0.98, 0.9, 0.91, 0.83, 0.69, and 0.85 respectively. The results of the suggested model are appropriate for anticipating garbage production based on population. The amount of garbage forecasts the number of workers and trucks required to handle garbage produced in a city. It demonstrates the transitive relationships between the population, the amount of garbage, and the workforce and vehicles. The suggested concept is best suited to boost municipal waste management organisations’ productivity and assist in creating a fresh design for smart cities.
- Akanksha, S., Bindushree, K., Rachana, R., Sharadhi, M. K., & Saravana, M. K. (2022). Efficient Garbage Management System Using Machine Learning. Perspectives in Communication, Embedded-systems and Signal-processing-PiCES, 5(10), 97-101.Google Scholar
- Masuduzzaman, M., Rahim, T., Islam, A., & Shin, S. Y. (2022). UxV-based Deep-Learning Integrated Automated and Secure Garbage Management Scheme using Blockchain. IEEE Internet of Things Journal.Google Scholar
- Liao, Y. H., & Juang, J. G. (2022). Real-Time UAV Trash Monitoring System. Applied Sciences, 12(4), 1838.Google Scholar
- Yuvaraj, N., Praghash, K., Raja, R. A., & Karthikeyan, T. (2022). An investigation of garbage disposal electric vehicles (GDEVs) integrated with deep neural networking (DNN) and intelligent transportation system (ITS) in smart city management system (SCMS). Wireless personal communications, 123(2), 1733-1752.Google Scholar
- Dwivedi, B. P., Kishore, M., Srivastava, S., & Shanker, S. (2022). IoT Based Smart Garbage Collector and Waste Management System. In Electronic Systems and Intelligent Computing (pp. 585-591). Springer, Singapore.Google Scholar
- Mishra, D., Vajire, S. L., Saxena, S., Gupta, P., Saini, D. K., Srivastava, A. K., & Rao, G. M. (2022). GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities. Computational Intelligence and Neuroscience, 2022.Google Scholar
- Poojary, H. D., Shinde, N. M., Singh, A. K., Kothuru, V., Kshirsagar, S. B., & Agarwal, H. (2022, May). Classification of Garbage For Robtic System Using Deep Learning Techniques. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1345-1348). IEEE.Google Scholar
- Medipelly, R., & Ijjina, E. P. (2022). A Fast Garbage Classification Model Based on Deep Learning. In IoT-Based Smart Waste Management for Environmental Sustainability (pp. 171-182). CRC Press.Google Scholar
- Rahman, M., Das, N. K., Rahat, F. R., & Alif, S. A. (2022). Waste management system using RFID and ultrasonic sensor (Doctoral dissertation, Brac University).Google Scholar
- 10. Peura, P., Voutilainen, O., & Kantola, J. (2022). From garbage to product and service systems: A longitudinal Finnish case study of waste management evolution. Waste Management, 140, 143-153.Google Scholar
- Chen, Z., Yang, J., Chen, L., & Jiao, H. (2022). Garbage classification system based on improved shufflenet v2. Resources, Conservation and Recycling, 178, 106090.Google Scholar
- Nooriman, W. M., Lim, R. Y., Rudzuan, M. N., Sofi, Y., Fauzi, M. M., & Abdullah, A. H. (2021, November). Design and Development of IoT based Garbage Monitoring and Management System. In Journal of Physics: Conference Series (Vol. 2107, No. 1, p. 012002). IOP Publishing.Google Scholar
- Prakash, B. L., Swetha, N., Gopi, P., & Kavya, P. (2021). Smart garbage monitoring and air pollution controlling system.Google Scholar
- Murugesan, S., Ramalingam, S., & Kanimozhi, P. (2021). Theoretical modelling and fabrication of smart waste management system for clean environment using WSN and IOT. Materials Today: Proceedings, 45, 1908-1913.Google Scholar
- Akter, O. (2021). Blockchain leveraged incentive providing waste management system. In Emerging Technologies in Data Mining and Information Security (pp. 429-437). Springer, Singapore.Google Scholar
- Priya, C., RamyaC, A. D., Pavithra, V., Shobika, S., & Dharini, M. (2021). Garbage Bin Management System. Annals of the Romanian Society for Cell Biology, 3593-3600.Google Scholar
- Ravanan, V., Subasri, R., Kumar, M. G. V., Dhivya, K. T., Kumar, P. S., & Roobini, K. (2021, October). Next Generation Smart Garbage Level Indication and Monitoring System using IoT. In 2021 Smart Technologies, Communication and Robotics (STCR) (pp. 1-4). IEEE.Google Scholar
- Karthikeyan, M., Subashini, T. S., & Jebakumar, R. (2021). SSD based waste separation in smart garbage using augmented clustering Nms. Automated Software Engineering, 28(2), 1-17.Google Scholar
- Diriba, D. B., & Meng, X. Z. (2021). Rethinking of the Solid Waste Management System of Addis Ababa, Ethiopia. Journal of Advances in Environmental Health Research, 9(1), 7-22.Google ScholarCross Ref
- Babu Loganathan, G., M Othman, M., & Tahsin Yasin, E. (2021). An Analysis on Garbage Removal Process by WSN thorugh Global System for Mobile Communication Media. REVISTA GEINTEC-GESTAO INOVACAO E TECNOLOGIAS, 11(3), 493-505.Google Scholar
- Sohag, M. U., & Podder, A. K. (2020). Smart garbage management system for a sustainable urban life: An IoT based application. Internet of Things, 11, 100255.. Smart garbage management system for a sustainable urban life: An IoT based application. Internet of Things, 11, 100255.Google Scholar
- Yang, Z., & Li, D. (2020). Wasnet: A neural network-based garbage collection management system. IEEE Access, 8, 103984-103993.Google Scholar
- Deng, Y., Zhang, J., Zhang, C., Ding, Z., Hao, C., & An, L. (2020). Countermeasures on plastic and microplastic garbage management. Microplastics in Terrestrial Environments, 447-469.Google Scholar
- Vinodha, D., Sangeetha, J., Sherin, B. C., & Renukadevi, M. (2020). Smart garbage system with garbage separation using object detection. International Journal of Research in Engineering, Science and Management.Google Scholar
- Tong, Y., Liu, J., & Liu, S. (2020). China is implementing “Garbage Classification” action. Environmental Pollution, 259, 113707.Google ScholarCross Ref
- Manikandan, R., Jamunadevi, S., Ajeyanthi, A., Divya, M., & Keerthana, D. (2019). An analysis of garbage mechanism for smart cities. Int. Res. J. Eng. Technol., 6(1), 1709-1713.Google Scholar
- https://www.niti.gov.in/sites/default/files/2021-12/Waste-Wise-Cities.pdf.Google Scholar
- Jassim MS, Coskuner G, Zontul M. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Management & Research. 2022;40(2):195-204. doi:10.1177/0734242X211008526.Google Scholar
Index Terms
- Regression Analysis of Garbage Management System for Smart Cities
Recommendations
Age-based garbage collection
Modern generational garbage collectors look for garbage among the young objects, because they have high mortality; however, these objects include the very youngest objects, which clearly are still live. We introduce new garbage collection algorithms, ...
Comments