Artificial Intelligence and Communication Technologies
Predicting Future Urban Growth in Bhopal, India using Artificial Intelligence based Modelling
Authors: Rahul Tiwari and Jayant Singh
Publishing Date: 18-02-2023
ISBN: 978-81-955020-5-9
Abstract
Urbanization is happening at a very fast rate, especially in developing countries like India. Urbanization is a challenge if not managed properly but an opportunity if it is planned and foreseen beforehand. Hence, predicting the urbanization trend becomes inadvertently important in order to properly plan for it. Various techniques have evolved over time, for predicting future urban growth, and with development in Artificial Intelligence and Image Processing domains, this field has opened up opportunities for better research and knowledge creation. In light of the same, the present study focuses on predictive modeling the future urban growth in the central city of India, Bhopal. The study uses land cover data provided by USGS Earth Explorer 2011 and thereafter uses ArcMap 10.7 and QGIS applications to develop an urban growth forecasting model, which is a hybrid model based on Cellular Automata and Fuzzy Urban Growth Modelling. The study concludes that predictions are totally based on the type of input data as machine learning works when data is accurate similarly here also these machine learning models will give more accurate predictions based on input data. In this case, the accuracy of prediction comes out to be 77% based on the data we feed into the model. Hence using more accurate data will give more accurate results, this study was an attempt to forecast the future growth of a city using new technological advancements like artificial intelligence and machine learning.
Keywords
Prediction, Urban growth, Cellular automata, Modeling
Cite as
Rahul Tiwari and Jayant Singh, "Predicting Future Urban Growth in Bhopal, India using Artificial Intelligence based Modelling", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2023, pp. 687-696. https://doi.org/10.52458/978-81-955020-5-9-65