Prognosis of urban environs using time series analysis for preventing overexploitation using artificial intelligence

Abstract

In the process of urban environment, the optimisation of network enactment is shifted from operation to maintenance and monitoring stage. During such conversion it is necessary to indicate the time series representation for preventing the overexploitation problem that happens due to more number of natural resources. It is necessary to use a set of historical data to check the behaviour of current state operations at varying time periods using an intelligent optimiser. Thus this study explores the implementation of time series analysis using artificial intelligence (AI) where accurate predictions are made in the entire urban environment even with big edifices. The major difference that is observed in the proposed method as compared to existing method is that two different boundary regions are chosen with distinct point values and only in two directions the monitoring device is installed. Since AI is involved in the entire process entire characteristics on forecasting current state procedure is represented using modified evolutionary optimisation (MEO) which observes entire biological nature of neighbouring environs. Additionally comparison analysis is made using MATLAB with five case studies where the proposed method proves to be much effective for about 70% as compared to existing models.

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.