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

Authors: S. Shitharth; Hariprasath Manoharan; Lakshmi Narayanan; Takkedu Malathi; S. Vatchala; Kommu Gangadhara Rao

Addresses: Department of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar, Ethiopia ' Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai – 600123, Tamil Nadu, India ' Gojan School of Business and Technology, Chennai, India ' Department of CSE, Nalla Malla Reddy Engineering College, Hyderabad, India ' School of Computer Science and Engineering, VIT Chennai Campus, India ' Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India

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.

Keywords: time series; urban environment; artificial intelligence; AI; forecast.

DOI: 10.1504/IJDATS.2023.132558

International Journal of Data Analysis Techniques and Strategies, 2023 Vol.15 No.1/2, pp.97 - 115

Received: 03 Jul 2022
Accepted: 23 Oct 2022
Published online: 28 Jul 2023 *