Predictive Waste Analytics Using Iot And Machine Learning For Smart Urban Resource Management

Abstract

The mushrooming urban population and the presenting resultant massive waste production offer very serious dilemmas to the city planners as well as resource managers. The drawbacks to traditional waste management methods are that they are reactively based and have less visibility, as well as providing inefficient, timely and sustainable waste management solutions. In a study conducted to analyse the situation, the authors suggest the integration of the predictive analytics framework that uses the IoT (Internet of Things) sensors and ML (machine learning) models to optimise municipal waste collection and disposal in smart urban locations. The system uses real time acquisition of the sensor-embedded bins, the GPS-enabled collection vehicles and the environmental conditions to predict the level of waste accumulation within the garbage collection vessels through time series and ensemble learning algorithms. Uncertainties in produce and distribution of the waste are modeled, as stochastic differential equations (SDEs). These uncertainties could depend on seasonal, demographic, economics variables. Results of simulation of real-world data of the city of Pune, India, indicate the high degree of efficiency in the collection route, fuel savings, and the significant decrease in waste overflow indicating the power of predictive models in nonlinear urban systems. The paper also sheds light on bifurcation patterns in waste trends in both policy intervention as well as high swings caused by festivals and natural occurrences. These findings indicate that a conjunction of ML with stochastic dynamics in our cities can guide more intelligent infrastructure investments as well as adaptive waste governance strategies. The given model is associated with Sustainable Development Goals (SDG 11 & 12), and it is possible to transfer it, providing a way of circular economy change, which is scalable and affordable.