MONITORING OF URBAN EXPANSION AND ITS IMPACT UISNG CELLULAR AUTOMATA MODELLING AND GEOINFORMATICS: A CASE STUDY OF DELHI

Abstract

                    The present study shows the spatio-temporal monitoring of land use and land cover monitoring of urban growth using cellular automata modeling over a (1994 to 2019) period of time of Delhi city. The study focused on the projecting the neighboring growth in the urban factor using Cellular Automata modeling Algorithm which provided a strong model calibration model. For this it requires a Landsat data also CBD data, Road proximity data, Population census data Development restricted data and Slope data. The study mainly based on projecting the urban growth so the LULC cover classification has been carried out also the distance from CBD distance from Road and population projection data prepared as per the model requirement. The satellite base LULC during the 1994 to 2019 shows the 273sq km which cloned 707.sq km of simulated urban growth. The resulted into the land use land cover data as to check weather is accurate or note some statistical model and methods  is applied to check the accuracy of the resulted data so in the present study principal component analysis is applied to the model which shows the high similarity between the actual and simulated LULC which is 73% to 85% also the confusion matrix is been calculated for the model accuracy which shows the 95% of accuracy of satellite based actual urban growth l and model based simulated urban growth. The projected LULC exhibits that the urban area will increase to 790 sq. km and 707 sq. km, primarily in western and eastern parts of Delhi city during 2014 to 2019 respectively. The rapid urban growth will replace and transform others LULC which losses the vegetation cover during the 2019-2024. The rapid urban growth is the critical issues for the many sustainable factors also impact on the human environment and ecosystem in Delhi. Therefore, the study inclined towards the decentralization of urban function and restoration of varied LULC in order to regulate the future urban growth pattern. The present algorithm requires a environment to run the code also some geospatial and statistical libraries should me necessary for importing thr data and checking the statistical data. Like NumPy, Matplotlib, GDAL, Sci-Learn.