Advanced CMD predictor screening approach coupled with cellular automata-artificial neural network algorithm for efficient land use-land cover change prediction

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 449, P. 141822 - 141822

Published: March 19, 2024

Language: Английский

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Land-Use Land-Cover Dynamics and Future Projections Using GEE, ML, and QGIS-MOLUSCE: A Case Study in Manisa

Sustainability, Journal Year: 2025, Volume and Issue: 17(4), P. 1363 - 1363

Published: Feb. 7, 2025

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical anticipate these transformations in order devise proactive urban policies implement sustainable planning practices that minimize negative impacts on ecosystems human livelihoods. This study investigates LULC changes the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery advanced machine learning algorithms. High-accuracy maps were generated for 2018, 2021, 2024 Random Forest, Support Vector Machine, k-Nearest Neighbors, Classification Regression Trees Among these, Forest algorithm demonstrated superior accuracy consistency distinguishing land-cover classes. Future scenarios 2027 2030 simulated Cellular Automata–Artificial Neural Network model QGIS MOLUSCE plugin. The results indicate significant growth, with built-up areas projected increase by 23.67% between 2030, accompanied declines natural resources bare land water bodies. highlights implications regarding ecological balance demonstrates importance integrating simulation models forecast use changes, enabling management. Overall, effective must be developed manage urbanization conduct a balanced manner.

Language: Английский

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Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia Lake basin

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(8), P. 3749 - 3764

Published: July 6, 2024

Language: Английский

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Synergistic approach for land use and land cover dynamics prediction in Uttarakhand using cellular automata and Artificial neural network

GEOMATICA, Journal Year: 2024, Volume and Issue: 76(2), P. 100017 - 100017

Published: Aug. 10, 2024

Alterations in Land use and cover (LULC) stand out as a key catalyst for shifts global climate patterns, environmental conditions, ecological dynamics. In order to further enhance our comprehension of the effects variability on environment, Remote sensing GIS analytical approaches have been thoroughly explored are reflected an imperative vision. Thus, objective this study is model Uttarakhand's LULC pattern 2032 analyse changes trend between 1992 2022. change mapping was conducted utilizing semi-automated hybrid classification approach high level accuracy which integrates both Maximum likelihood Object based image analysis techniques Landsat datasets. The machine learning Cellular automata Artificial neural networks (CA-ANN) within MOLUSCE plugin QGIS applied future patterns. assessment results showed that overall years 1992, 2002, 2012, 2022 96.94 %, 97.77 98.61 % 98.87 respectively, kappa statistics coefficient 0.92, 0.95, 0.94 0.95 respectively. simulated projected map implies substantially accuracy, with Kappa value 0.77 85.39 correctness. Then, year predicted using CA-ANN. observed alterations significant, characterized by augmentation built-up areas, open land, water bodies, alongside decline snow-covered regions, vegetation cover. Whereas, slight increase seen Forested areas. Planners policy makers aiming accomplish more sustainable efficient management environment will find over prolonged period time be useful asset optimal land planning.

Language: Английский

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Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 154 - 154

Published: Jan. 13, 2025

Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities population growth negatively This negative impact directly relates to climate change, sustainable agriculture, inflation, food security at local global levels. Remote sensing GIS tools can provide valuable information about change detection. study examines correlation between rate LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, Dera Ghazi Khan—over a 30-year period from 2003 2033. Landsat 7, 8, Sentinel-2 satellite imagery within Google Earth Engine (GEE) cloud platform was utilized create 2003, 2013, 2023 maps via supervised classification with random forest (RF) classifier, which is subset artificial intelligence (AI). achieved over 90% overall accuracy kappa value 0.9 for classified maps. into built-up, vegetation, water, barren classes Multan an additional “rock” class included Khan due its unique topography. (2003, 2023) were prepared validated using Engine. Future predictions 2033 generated MOLUSCE model QGIS. The results indicated substantial urban expansion as built-up areas increased 8.36% 25.56% 2033, vegetation displaying decreasing trends 82.96% 70% 7.95% 3.5%, respectively. Moreover, containing water fluctuated ultimately changed 0.73% 0.9% In grew 1.33% 5.80% while decreased 79.13% 74.31%. expressed significant increases 2.29% 12.21% 22.53% 44.72%, respectively, alongside reductions rock 32.82% 10.83% 41.23% 31.2%, Population projections compound each district emphasize demographic on changes. These findings focus need policies manage unplanned sprawl environmentally practices. provides critical awareness policy makers planners aiming balance environmental sustainability.

Language: Английский

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Change Monitoring and Assessment of Land Use and Land Cover for the Municipality of Ouessè in Benin by the Modules for Land Use Change Evaluation Plugin

International Journal of Agricultural and Environmental Information Systems, Journal Year: 2025, Volume and Issue: 16(1), P. 1 - 18

Published: Feb. 15, 2025

Assessing land use and cover (LULC) changes is crucial in sub-Saharan Africa due to population growth degradation, emphasizing the significance of effective planning management. In this study, authors used modules for change simulations plugin within quantum geographic information system software analyze predict LULC Ouessè municipality. The Landsat images a cellular automata-artificial neural network model future scenarios. Major categories included woodlands, agricultural land, plantations, rock domes. Changes these were observed between 1986 2019, with predictions showing further shifts until 2049. This approach allowed detailed accurate assessment over time, providing valuable insights into dynamics study area. Smart agriculture livestock technologies, along business models, are needed achieve sustainable balance human activities natural resources.

Language: Английский

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Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata

Urban Lifeline, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 25, 2025

Abstract Savar, a newly developed suburb of Dhaka, is rapidly urbanizing due to various socioeconomic and environmental factors. This study was conducted evaluate temporal spatial changes in Land Use Cover (LULC) for the years 1980, 2000, 2020 predict future LULC changes. Supervised classification algorithms cellular automata model based on Artificial Neural Networks (ANN) were used prepare maps simulations. The methodology designed overcome limitations traditional land use cover change modeling, including low accuracy, computational inefficiency, limited adaptability complex patterns. revealed that rate built-up area increased significantly over 40 while barren agricultural decreased drastically. Future simulation results illustrated would increase by 95.07 km 2 (33.29%) 2040. model's prediction growth areas 2040 demonstrated significant rise urban coverage with an accuracy 41.14%. Therefore, this will help us understand present dynamics along trend assist planners, policymakers, stakeholders sustainable planning techniques management.

Language: Английский

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Forecasting shoreline dynamics and land use/land cover changes in Balukhand-Konark Wildlife Sanctuary (India) using geospatial techniques and machine learning

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 975, P. 179207 - 179207

Published: April 7, 2025

Language: Английский

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Multitemporal Assessment of Ecosystem Service Values and Carbon Sequestration in a Protected Ecosystem: A Case Study from Dalma Wildlife Sanctuary in Jharkhand, India

International Journal of Environmental Research, Journal Year: 2025, Volume and Issue: 19(4)

Published: April 17, 2025

Language: Английский

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Change Detection Using Machine Learning Algorithms in Google Earth Engine Environment Bharatpur District of the Rajasthan State

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 385 - 399

Published: Jan. 1, 2025

Language: Английский

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