Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata
Abstract Savar, a newly developed suburb of Dhaka, is rapidly urbanizing due to various socioeconomic and environmental factors. This study was conducted to evaluate temporal and spatial changes in Land Use and Land Cover (LULC) for the years 1980, 2000, and 2020 and predict future LULC changes. Sup...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-03-01
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| Series: | Urban Lifeline |
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| Online Access: | https://doi.org/10.1007/s44285-025-00039-2 |
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| author | Tania Yeasmin Sourav Karmaker Md Shafiqul Islam Irteja Hasan Saifur Rahman Mahmudul Hasan |
| author_facet | Tania Yeasmin Sourav Karmaker Md Shafiqul Islam Irteja Hasan Saifur Rahman Mahmudul Hasan |
| author_sort | Tania Yeasmin |
| collection | DOAJ |
| description | Abstract Savar, a newly developed suburb of Dhaka, is rapidly urbanizing due to various socioeconomic and environmental factors. This study was conducted to evaluate temporal and spatial changes in Land Use and Land Cover (LULC) for the years 1980, 2000, and 2020 and predict future LULC changes. Supervised classification algorithms and cellular automata model based on Artificial Neural Networks (ANN) were used to prepare LULC maps and future simulations. The methodology was designed to overcome limitations in traditional land use and land cover change modeling, including low accuracy, computational inefficiency, and limited adaptability to complex spatial patterns. The study revealed that the rate of built-up area increased significantly over 40 years while barren land and agricultural land decreased drastically. Future LULC simulation results illustrated that the built-up area would increase by 95.07 km2 (33.29%) in 2040. The model's prediction of the growth of built-up areas by 2040 demonstrated a significant rise in urban coverage with an accuracy rate of 41.14%. Therefore, this study will help us to understand the present and future urban land dynamics along with the trend of temporal and spatial LULC changes that assist planners, policymakers, and stakeholders in sustainable urban planning techniques and urban land management. |
| format | Article |
| id | doaj-art-aa5c230a0ea2422e9f06e82df191c54f |
| institution | OA Journals |
| issn | 2731-9989 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Urban Lifeline |
| spelling | doaj-art-aa5c230a0ea2422e9f06e82df191c54f2025-08-20T02:10:20ZengSpringer NatureUrban Lifeline2731-99892025-03-013111610.1007/s44285-025-00039-2Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automataTania Yeasmin0Sourav Karmaker1Md Shafiqul Islam2Irteja Hasan3Saifur Rahman4Mahmudul Hasan5GIS & RS SolutionFaculty of Computer Science, Mathematics and Geoinformatics, Stuttgart University of Applied SciencesGIS & RS SolutionDepartment of Coastal Studies and Disaster Management, University of BarishalDepartment of Geo-Information Science and Earth Observations, Patuakhali Science and Technology UniversityDepartment of Geo-Information Science and Earth Observations, Patuakhali Science and Technology UniversityAbstract Savar, a newly developed suburb of Dhaka, is rapidly urbanizing due to various socioeconomic and environmental factors. This study was conducted to evaluate temporal and spatial changes in Land Use and Land Cover (LULC) for the years 1980, 2000, and 2020 and predict future LULC changes. Supervised classification algorithms and cellular automata model based on Artificial Neural Networks (ANN) were used to prepare LULC maps and future simulations. The methodology was designed to overcome limitations in traditional land use and land cover change modeling, including low accuracy, computational inefficiency, and limited adaptability to complex spatial patterns. The study revealed that the rate of built-up area increased significantly over 40 years while barren land and agricultural land decreased drastically. Future LULC simulation results illustrated that the built-up area would increase by 95.07 km2 (33.29%) in 2040. The model's prediction of the growth of built-up areas by 2040 demonstrated a significant rise in urban coverage with an accuracy rate of 41.14%. Therefore, this study will help us to understand the present and future urban land dynamics along with the trend of temporal and spatial LULC changes that assist planners, policymakers, and stakeholders in sustainable urban planning techniques and urban land management.https://doi.org/10.1007/s44285-025-00039-2UrbanizationLand useLand coverArtificial neural networkLand use predictions |
| spellingShingle | Tania Yeasmin Sourav Karmaker Md Shafiqul Islam Irteja Hasan Saifur Rahman Mahmudul Hasan Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata Urban Lifeline Urbanization Land use Land cover Artificial neural network Land use predictions |
| title | Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata |
| title_full | Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata |
| title_fullStr | Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata |
| title_full_unstemmed | Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata |
| title_short | Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata |
| title_sort | prediction of land cover changes in an urban city of bangladesh using artificial neural network based cellular automata |
| topic | Urbanization Land use Land cover Artificial neural network Land use predictions |
| url | https://doi.org/10.1007/s44285-025-00039-2 |
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