# Municipal solid waste management forecasting using neural networks at discharge point scale

## TL;DR

This paper introduces a neural network-based system to predict waste generation at specific urban waste discharge points, aiming to improve waste collection efficiency and sustainability.

## Contribution

The novelty lies in using localized, data-driven predictions at individual discharge points rather than aggregated waste indicators.

## Key findings

- Data-driven approaches can improve waste collection planning by incorporating contextual and temporal information.
- Accurate short-term forecasts enable proactive waste management, reducing operational costs and environmental impact.

## Abstract

Urbanisation and population growth continue to accelerate waste generation, posing serious environmental and logistical challenges for the management of Municipal Solid Waste (MSW) management. The present study proposes a predictive framework for forecasting the behaviour of individual Discharge Points (DPs), with the view to enhancing decision-making in urban waste management. The necessity for localised predictions that extend beyond the scope of aggregated waste indicators is identified by research. Furthermore, it addresses the requirement for finer predictive granularity, which is capable of capturing the dynamic variations observed across DPs. The findings underscore the potential of data-driven approaches to facilitate more efficient, scalable, and intelligent waste collection planning in urban contexts by the incorporation of contextual and temporal information. By enabling accurate short-term forecasts, the proposed approach facilitates the transition from reactive to proactive collection planning, reducing operational cost and environmental footprints. Overall, the research contributes to advancing data-driven strategies for sustainable MSW management and demonstrates the potential of AI-based predictive model to support intelligent and scalable urban waste collection systems.