Big Data Analytics Platform for Urban Environmental Monitoring and Optimization

MIT-School of Computing, MIT-ADT, Loni Kalbhor, Pune 412201

^* Corresponding author: amit.gadekar@mituniversity.edu.in

Published online: 20 November 2025

Abstract

Environmental monitoring in the city demands the solution which will enable to work with the huge volumes of data of different kinds and utilize the resources in the most effective way. The presented work proposes Big Data Analytics Platform to address the urban problems with the help of the improved algorithms. Preprocessing Data Aggregation and Preprocessing Algorithms Preprocessing Data Aggregation and Preprocessing Algorithms obtain and standardize different data to ensure that the data result of the analysis is standardized. The temporal and spatial relationships are determined using the Spatial Temporal Data Analysis Algorithms before explaining the urban dynamics. The learning based on the Machine Learning-Based Predictive Modelling Algorithms and offer the correct environmental conditions and trends within the urban regions to make timely decisions. Multi-Objective Optimization Algorithms are employed when there are resources to be controlled through the fulfillment of groups of competing goals including energy use and emission issues. These two algorithms provide the Real-Time Data Visualization and Reporting and expose the stakeholders to the relevant information in the format that will be understood by them. In general, one can refer to these algorithms as a strong foundation of the overall monitoring and management of sustainable environments in cities, and thus creation of sustainable cities. The fact that the control of emissions, traffic simulation, and waste disposal sustainability concepts are among the areas of discussion.