Assessment and prediction of mega-infrastructure projects on rural ecosystems using machine learning algorithms.
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| Title: | Assessment and prediction of mega-infrastructure projects on rural ecosystems using machine learning algorithms. |
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| Authors: | Morshed, Md. Manjur1 (AUTHOR) mmorshed@urp.kuet.ac.bd, Fattah, Md. Abdul1 (AUTHOR) mafattah.kuet@gmail.com, Morshed, Syed Riad1 (AUTHOR) riad.kuet.urp16@gmail.com, Sydunnaher, Sumya2 (AUTHOR) sumya@idm.kuet.ac.bd |
| Source: | Environment, Development & Sustainability. Feb2026, Vol. 28 Issue 2, p4127-4149. 23p. |
| Subject Terms: | *Random forest algorithms, *Landscape changes, *Machine learning, *Infrastructure (Economics), *Rurality, *Urbanization, *Environmental management, *Environmental impact analysis |
| Geographic Terms: | Bangladesh |
| Abstract: | Large-infrastructures, while instrumental in fostering economic growth and providing critical service facilities, often pose significant threats to regional sustainability through ecological and environmental degradation. Therefore, understanding and simulating the impacts of mega-infrastructure projects on ecosystems is critical for sustainable environmental management. This study critically investigated and simulated the potential impacts of megaprojects on land cover (LULC) change and urbanization in rural ecosystems, with a specific focus on the Padma Multipurpose Bridge (PMB) in Bangladesh, the nation's largest infrastructure endeavor. Applying the Random Forest algorithm on Landsat imagery spanning two decades (2003–2023), we quantitatively assessed the spatial LULC changes. Moreover, we assessed spatial urban expansion dynamics attributed to the PMB by calculating the annual urban expansion rate and employing the urban expansion differentiation index (UEDI). Results revealed a substantial transformation in the Munshiganj district, characterized by a loss of 261.90 km2 of vegetated areas alongside increase in built-up, cropland, and barren soil areas by 19.11, 141.80, and 84.11 km2, respectively. UEDI analysis shows that PMB construction increased the urbanization rate in the northwestern region of Munshiganj. Projections using the Cellular Automata Artificial Neural Network model suggest a 74.12% increase in built-up areas by 2033, predominantly around Munshiganj Upazila, and along the Dhaka-Mawa highway. Future UEDI suggest that all Upazilas in Munshiganj will experience fast to high-speed urban expansion rates, will substantially reduce the vegetation and cropland areas. These underscore the pressing need for integrating sustainable environmental management practices in the planning and implementation phases of megaprojects to mitigate adverse ecological impacts. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 192482144 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessment and prediction of mega-infrastructure projects on rural ecosystems using machine learning algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Morshed%2C+Md%2E+Manjur%22">Morshed, Md. Manjur</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mmorshed@urp.kuet.ac.bd</i><br /><searchLink fieldCode="AR" term="%22Fattah%2C+Md%2E+Abdul%22">Fattah, Md. Abdul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mafattah.kuet@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Morshed%2C+Syed+Riad%22">Morshed, Syed Riad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> riad.kuet.urp16@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sydunnaher%2C+Sumya%22">Sydunnaher, Sumya</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sumya@idm.kuet.ac.bd</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Feb2026, Vol. 28 Issue 2, p4127-4149. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Landscape+changes%22">Landscape changes</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Rurality%22">Rurality</searchLink><br />*<searchLink fieldCode="DE" term="%22Urbanization%22">Urbanization</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+management%22">Environmental management</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+impact+analysis%22">Environmental impact analysis</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Bangladesh%22">Bangladesh</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large-infrastructures, while instrumental in fostering economic growth and providing critical service facilities, often pose significant threats to regional sustainability through ecological and environmental degradation. Therefore, understanding and simulating the impacts of mega-infrastructure projects on ecosystems is critical for sustainable environmental management. This study critically investigated and simulated the potential impacts of megaprojects on land cover (LULC) change and urbanization in rural ecosystems, with a specific focus on the Padma Multipurpose Bridge (PMB) in Bangladesh, the nation's largest infrastructure endeavor. Applying the Random Forest algorithm on Landsat imagery spanning two decades (2003–2023), we quantitatively assessed the spatial LULC changes. Moreover, we assessed spatial urban expansion dynamics attributed to the PMB by calculating the annual urban expansion rate and employing the urban expansion differentiation index (UEDI). Results revealed a substantial transformation in the Munshiganj district, characterized by a loss of 261.90 km2 of vegetated areas alongside increase in built-up, cropland, and barren soil areas by 19.11, 141.80, and 84.11 km2, respectively. UEDI analysis shows that PMB construction increased the urbanization rate in the northwestern region of Munshiganj. Projections using the Cellular Automata Artificial Neural Network model suggest a 74.12% increase in built-up areas by 2033, predominantly around Munshiganj Upazila, and along the Dhaka-Mawa highway. Future UEDI suggest that all Upazilas in Munshiganj will experience fast to high-speed urban expansion rates, will substantially reduce the vegetation and cropland areas. These underscore the pressing need for integrating sustainable environmental management practices in the planning and implementation phases of megaprojects to mitigate adverse ecological impacts. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-024-05133-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 4127 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Landscape changes Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Infrastructure (Economics) Type: general – SubjectFull: Rurality Type: general – SubjectFull: Urbanization Type: general – SubjectFull: Environmental management Type: general – SubjectFull: Environmental impact analysis Type: general – SubjectFull: Bangladesh Type: general Titles: – TitleFull: Assessment and prediction of mega-infrastructure projects on rural ecosystems using machine learning algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Morshed, Md. Manjur – PersonEntity: Name: NameFull: Fattah, Md. Abdul – PersonEntity: Name: NameFull: Morshed, Syed Riad – PersonEntity: Name: NameFull: Sydunnaher, Sumya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 2 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
