AI-enabled geospatial solutions for waste collection and forecasting for smart cities application

Paper Title: AI-enabled geospatial solutions for waste collection and forecasting for smart cities application: insights from Kathmandu municipality

Authors: Krishna Bikram Shah, S. Visalakshi, Ranjit Panigrahi

Corresponding Author: Krishna Bikram Shah (krishnabs@nec.edu.np), India

 

Abstract

Effective waste management is a critical global concern, especially in urban areas where efficient systems are essential for reducing litter, minimizing environmental contamination, and enhancing urban aesthetics. This study presents a comprehensive framework for optimizing waste management in Kathmandu Municipality, focusing on the spatial allocation of collection points, bin requirements, and predictive waste level modeling. This approach is based on the primary parameters of the rate at which waste is generated, the capacity of a bin, the density of waste, and the frequency of collection. The model also accommodates waste segregation-this means effective bin deployment across categories of waste to avoid wasting resources. It includes a time-series forecasting model, simulating waste accumulation for 7 days with seasonality influences, holiday influence, fluctuation of the population, and socio-economic influence. Trend of generation waste is reported at an interval of 6 hours in order to enhance precision within the schedule of collecting wastes. Lower risk of overflow of the bins due to the services before bins overflow. This holistic framework shall consequently provide data-driven scalable solutions to Kathmandu Municipality in optimizing its routes for collecting wastes, enhancing resource efficiency, and adapting the patterns of producing wastes on a real-time basis.

Keywords

Waste Management, Waste Collection Point location-allocation, Bin location-allocation, Sustainability, Kathmandu Municipality

 

Cite:

Shah, K. B., Visalakshi, S., & Panigrahi, R. . (2025). AI-enabled geospatial solutions for waste collection and forecasting for smart cities application: insights from Kathmandu municipality. Future Technology, 4(1), 37–49. Retrieved from https://fupubco.com/futech/article/view/251