Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

Annals of GIS, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 28

Published: May 4, 2024

The study examines the complex dynamics of changes in LULC over three decades, focused on years 1992, 2002, 2012, and 2022. research highlights significance comprehending these alterations within framework environmental socio-economic consequences. land use cover (LULC) have significant far-reaching effects ecosystems, biodiversity, human livelihoods. This offers useful information for politicians, conservationists, urban planners by examining historical patterns forecasting future changes. utilized a Multilayer Perceptron Neural Network (MLP-NN), well-known machine learning technique that excels at collecting intricate patterns. model's design had layers: input, hidden, output. model underwent 10,000 iterations during its training process, thorough statistical analysis was conducted to assess impact each driving component. MLP-NN demonstrated impressive performance, with skill measure 0.8724 an accuracy rate 89.08%. estimates 2022 verified comparing them observed data, ensuring reliability. Moreover, presence evidence likely found be factor substantial model. effectiveness accurately predicting LULC. exceptional proficiency make it powerful tool forecasts. Identifying primary causes performance understanding their implications may help enhance management strategies, encourage spatial planning, guide accurate decision-making, facilitate development policies align sustainable growth development.

Language: Английский

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Quantifying LULC changes in Urmia Lake Basin using machine learning techniques, intensity analysis and a combined method of cellular automata (CA) and artificial neural networks (ANN) (CA-ANN)

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 10(2), P. 2011 - 2030

Published: Nov. 26, 2023

Language: Английский

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Space-Time Estimation of Built-Up Landscape of Ilorin Metropolis, Nigeria

Papers in Applied Geography, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: March 21, 2025

Language: Английский

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Land Use Changes and Their Driving Factors in the Liuchong River Basin Based on the Geographical Detector Model

Journal of Resources and Ecology, Journal Year: 2025, Volume and Issue: 16(2)

Published: April 4, 2025

Language: Английский

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Landscape transition-induced ecological risk modeling using GIS and remote sensing techniques: a case of Saint Martin Island, Bangladesh

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(10)

Published: Sept. 21, 2024

Language: Английский

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Potential contribution of land cover change on flood events in the Senegal River basin

Frontiers in Water, Journal Year: 2024, Volume and Issue: 6

Published: Sept. 18, 2024

The increase in flood events observed West African countries, and often specific river basins, can be influenced by several factors, including anthropogenic land use land-cover changes. However, the potential contribution of cover changes to still needs explored, especially Africa. Here, fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model additionally incorporates surface, subsurface, overland flow, channel routing, is used investigate impact a change scenario on Senegal River basin. simulation was performed from 2010 2020, with calibration period spanning 2011 2012 validation 2013 2020. Several skill scores, Nash-Sutcliffe Efficiency (NSE), BIAS, Kling-Gupta (KGE), were utilized assess performances. Additionally, two planetary boundary layer schemes (PBL5 PBL7) determine their associated uncertainty. Our results show that best (NSE = 0.70; KGE 0.83; PBIAS −7% BE 0.67) basin are obtained PBL5 when SLOPE parameter 0.03. A similar good performance also for NSE 0.74, 0.84, −8%. Likewise, our findings indicate converting savanna woody savannas elevate water resources, 2% rise precipitation 4% runoff. This transition correlates moderate (3500–4000 m 3 /s), decrease severe floods (4000–5000 occurrence extreme (>5000 /s)

Language: Английский

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Modeling Spatiotemporal Land Use/Land Cover Dynamics by Coupling Multilayer Perceptron Neural Network and Cellular Automata Markov Chain Algorithms in the Wabe River Catchment, Omo Gibe River Basin, Ethiopia

Environmental Research Communications, Journal Year: 2024, Volume and Issue: 6(10), P. 105011 - 105011

Published: Sept. 27, 2024

Abstract Land Use/Land Cover (LULC) change has been a substantial environmental concern, hindering sustainable development over the past few decades. To that end, comprehending and future patterns of LULC is vital for conserving sustainably managing land resources. This study aimed to analyze spatiotemporal landscape dynamics from 1986 2022 predict situations 2041 2058, considering business-as-usual (BAU) scenario in Wabe River Catchment. The historical use image classification employed supervised technique using maximum likelihood algorithms ERDAS Imagine, identified six major cover classes. For projections changes multilayer perceptron neural network cellular automata-Markov chain were utilized, incorporating various driving factors independent spatial datasets. findings revealed significant ongoing catchment, with persistent trends expected. Notably, woodland, built-up areas, agriculture experienced net increases by 0.24%, 1.96%, 17.22% respectively, while grassland, forest, agroforestry faced notable decreases 4.65%, 3.58%, 11.20% respectively 2022. If current rate continues, agricultural lands will expand 1.28% 5.07%, forest decline 2.69% 3.63% 2058. However, woodland grassland exhibit divergent patterns, projected decrease 0.57% an anticipated increase 0.54% cover. Overall, observed indicated shift towards intensive agriculture, area expansion, potentially adverse consequences such as soil degradation, biodiversity loss, ecosystem decline. mitigate these promote development, immediate action necessary, including environmentally friendly conservation approaches, management practices, habitat protection, reforestation efforts, ensuring long-term resilience viability catchment’s ecosystems.

Language: Английский

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Scenario Analysis of Shorelines, Coastal Erosion, and Land Use/Land Cover Changes and Their Implication for Climate Migration in East and West Africa

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 1081 - 1081

Published: June 26, 2024

Climate change-induced sea level rise, shoreline changes, and coastal erosion are projected to drive massive population displacement mobility in Africa. This study was conducted examine the pattern of erosion, land use/land cover dynamics, projections, their implications on internal migration Senegal, Kenya, Tanzania, representing West East The digitized mapped into accretion, trend analysis, which further explains vulnerability physical processes that could trigger human within context environmental/climate migration. Analysis use dynamics obtained from Landsat 5 TM 1986, 7 ET 2006, 8 OLI/TIRS 2016, 9 2022 computed using ArcGIS 10.7 for land-use change percentage square kilometers contributions risk regions. outcome analysis reveals 972.03 sqkm has been lost Senegal 1986 with 2016–2022 described as period highest terms loss. In −463.30 also agents wave processes, 1986–2006 recording share −87.74% loss valuable land, while −1033.35 2006–2016 alone −10.4634% result indicates a vegetation significant increase settlement urbanization. scenario at 10, 20, 30 m 567 persons per 10 m, 25,904.6 20 25,904.5 will be displaced m. 57,746 1210.5 7737.32 maximum density is 10,260.97 sqkm. Structured questionnaires were administered elicit responses dwellers perception climate part ground truthing survey affirms its exposure major drivers area.

Language: Английский

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Land-use/cover change and future prediction by integrating the ML techniques of random forest and CA-Markov chain model of the Ganges alluvial tract of Eastern India

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 25, 2024

Language: Английский

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