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LULC Dynamics Study and Modeling of Urban Land Expansion Using CA-ANN
https://orcid.org/http://orcid.org/0000-0002-2655-1797
https://orcid.org/http://orcid.org/0000-0001-6696-8098
Owing to the rapid population growth and urbanization rate, most cities are affected by land use, and land cover (LULC) changes through urban area expansion. A feasibility study was proposed to examine the LULC changes and urban land expansion pattern for Kakinada city in Andhra Pradesh, as it was recently announced as one of the potential smart cities in India. The dynamics of LULC changes detection and prediction analysis were performed in QGIS 2.18.16 with the help of a cellular automata-based artificial neural network (CA-ANN) simulation model. The Landsat 5 and Landsat 8 satellite images from 1990 to 2020 are used for classification using the maximum likelihood classification (MLC) algorithm in ERDAS IMAGINE 2013. The image classification was conducted on five different LULC classes (i) agricultural, (ii) built-up, (iii) vegetation, (iv) wetlands, and (v) water bodies. The CA-ANN model was trained with the study area's explanatory variables (i.e., elevation, slope, population density, and distances from roads, settlement areas, and stream networks). Subsequently, the trained CA-ANN model was used to determine the dynamics of LULC changes from 1990 to 2020 and predict future urban sprawl by 2030 and 2040. The results indicate that the urban area in the city increased rapidly by 18.02% of the total area (906.79 km2) of the study area from 1990 to 2040, which mainly showed in the cost of vegetation and wetlands. Kakinada City is expanding in the direction of its bordering region with the conversion of rural, sub-urban regions into urban sprawls. A correlation analysis was also carried out between the explanatory variables and LULC changes, which revealed that all the explanatory variables significantly influenced the urbanization expansion in the study region. Urban expansion occurs haphazardly in the study region, which may lead to the in-equilibrium of environmental and anthropogenic activities. Since the study area is declared a smart city, a balance must be found between making the city smart and preserving the natural environment and cultural artifacts. Before making the city smart, the notions of urban planning are to be applied to the conservation and management of natural land use classes which will enhance the quality of life in an urban environment in the study region.
LULC Dynamics Study and Modeling of Urban Land Expansion Using CA-ANN
https://orcid.org/http://orcid.org/0000-0002-2655-1797
https://orcid.org/http://orcid.org/0000-0001-6696-8098
Owing to the rapid population growth and urbanization rate, most cities are affected by land use, and land cover (LULC) changes through urban area expansion. A feasibility study was proposed to examine the LULC changes and urban land expansion pattern for Kakinada city in Andhra Pradesh, as it was recently announced as one of the potential smart cities in India. The dynamics of LULC changes detection and prediction analysis were performed in QGIS 2.18.16 with the help of a cellular automata-based artificial neural network (CA-ANN) simulation model. The Landsat 5 and Landsat 8 satellite images from 1990 to 2020 are used for classification using the maximum likelihood classification (MLC) algorithm in ERDAS IMAGINE 2013. The image classification was conducted on five different LULC classes (i) agricultural, (ii) built-up, (iii) vegetation, (iv) wetlands, and (v) water bodies. The CA-ANN model was trained with the study area's explanatory variables (i.e., elevation, slope, population density, and distances from roads, settlement areas, and stream networks). Subsequently, the trained CA-ANN model was used to determine the dynamics of LULC changes from 1990 to 2020 and predict future urban sprawl by 2030 and 2040. The results indicate that the urban area in the city increased rapidly by 18.02% of the total area (906.79 km2) of the study area from 1990 to 2040, which mainly showed in the cost of vegetation and wetlands. Kakinada City is expanding in the direction of its bordering region with the conversion of rural, sub-urban regions into urban sprawls. A correlation analysis was also carried out between the explanatory variables and LULC changes, which revealed that all the explanatory variables significantly influenced the urbanization expansion in the study region. Urban expansion occurs haphazardly in the study region, which may lead to the in-equilibrium of environmental and anthropogenic activities. Since the study area is declared a smart city, a balance must be found between making the city smart and preserving the natural environment and cultural artifacts. Before making the city smart, the notions of urban planning are to be applied to the conservation and management of natural land use classes which will enhance the quality of life in an urban environment in the study region.
LULC Dynamics Study and Modeling of Urban Land Expansion Using CA-ANN
https://orcid.org/http://orcid.org/0000-0002-2655-1797
https://orcid.org/http://orcid.org/0000-0001-6696-8098
Owing to the rapid population growth and urbanization rate, most cities are affected by land use, and land cover (LULC) changes through urban area expansion. A feasibility study was proposed to examine the LULC changes and urban land expansion pattern for Kakinada city in Andhra Pradesh, as it was recently announced as one of the potential smart cities in India. The dynamics of LULC changes detection and prediction analysis were performed in QGIS 2.18.16 with the help of a cellular automata-based artificial neural network (CA-ANN) simulation model. The Landsat 5 and Landsat 8 satellite images from 1990 to 2020 are used for classification using the maximum likelihood classification (MLC) algorithm in ERDAS IMAGINE 2013. The image classification was conducted on five different LULC classes (i) agricultural, (ii) built-up, (iii) vegetation, (iv) wetlands, and (v) water bodies. The CA-ANN model was trained with the study area's explanatory variables (i.e., elevation, slope, population density, and distances from roads, settlement areas, and stream networks). Subsequently, the trained CA-ANN model was used to determine the dynamics of LULC changes from 1990 to 2020 and predict future urban sprawl by 2030 and 2040. The results indicate that the urban area in the city increased rapidly by 18.02% of the total area (906.79 km2) of the study area from 1990 to 2040, which mainly showed in the cost of vegetation and wetlands. Kakinada City is expanding in the direction of its bordering region with the conversion of rural, sub-urban regions into urban sprawls. A correlation analysis was also carried out between the explanatory variables and LULC changes, which revealed that all the explanatory variables significantly influenced the urbanization expansion in the study region. Urban expansion occurs haphazardly in the study region, which may lead to the in-equilibrium of environmental and anthropogenic activities. Since the study area is declared a smart city, a balance must be found between making the city smart and preserving the natural environment and cultural artifacts. Before making the city smart, the notions of urban planning are to be applied to the conservation and management of natural land use classes which will enhance the quality of life in an urban environment in the study region.
LULC Dynamics Study and Modeling of Urban Land Expansion Using CA-ANN
Lecture Notes in Civil Engineering
Swain, Bibhu Prasad (editor) / Dixit, Uday Shanker (editor) / Roy, Srija (author) / Chintalacheruvu, Madhusudana Rao (author)
International Conference on Science, Technology and Engineering ; 2023 ; Manipur, India
2023-08-22
12 pages
Article/Chapter (Book)
Electronic Resource
English