Application of Event Detection to Improve Waste Management Services in Developing Countries

Mohd Anjum (), Sana Shahab and Mohammad Sarosh Umar
Additional contact information
Mohd Anjum: Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India
Sana Shahab: Department of Business Administration, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammad Sarosh Umar: Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India

Sustainability, 2022, vol. 14, issue 20, 1-20

Abstract: This study illustrates a proof-of-concept model to improve solid waste management (SWM) services by analyzing people’s behavior towards waste. A deep neural network model is implemented to detect and identify the specific types of events/activities in the proximity of the waste bin. This model consists of a three-dimensional convolutional neural network (3D CNN) and a long short-term memory (LSTM)-based recurrent neural network. The model was trained and tested over a handcrafted data set and achieved an average precision of 0.944–0.986. This precision is promising to support the implementation of the model on a large scale in the actual environment. The performance measures of all individual events indicate that the model successfully detected the individual events and has high precision for classifying them. The study also designed and built an experimental setup to record the data set, which comprises 3200 video files duration between 150–1200 s. Methodologically, the research is supported through a case study based on the recorded data set. In this case study, the frequencies of identified events/activities at a bin are plotted and thoroughly analyzed to determine people’s behavior toward waste. This frequency analysis is used to determine the locations where one of the following actions is required to improve the SWM service: (i) people need to be educated about the consequences of waste scattering; (ii) bin capacity or waste collection schedules are required to change; (iii) both actions are required simultaneously; (iv) none of the actions are needed.

Keywords: convolution neural networks; event detection; municipal solid waste; people’s behavior; recurrent neural network; solid waste management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13189/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13189/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13189-:d:942027

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().