Spatiotemporal dynamics of land use land cover patterns in the middle Omo-Gibe River Basin, Ethiopia: machine learning, geospatial, and field survey integrated approach DOI
Abera Abiyo Dofee, Pritam Chand

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 31, 2024

Language: Английский

Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach DOI Creative Commons
Khadim Hussain, Kaleem Mehmood,

Yujun Sun

et al.

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

Citations

20
Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha DOI
Abhijeet Das

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

Citations

1
Land-use simulation for synergistic pollution and carbon reduction: Scenario analysis and policy implications DOI
Luyan Wu, Yanhu He,

Qian Tan

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 356, P. 120603 - 120603

Published: March 21, 2024

Language: Английский

Citations

6
Forecasting shoreline dynamics and land use/land cover changes in Balukhand-Konark Wildlife Sanctuary (India) using geospatial techniques and machine learning DOI
Manoranjan Mishra, Debdeep Bhattacharyya,

Brihaspati Mondal

et al.

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

Published: April 7, 2025

Language: Английский

Citations

0
LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa DOI Creative Commons
Orlando Bhungeni, Michael Gebreslasie,

Ashadevi Ramjatan

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: April 30, 2025

Water catchment areas are the key strategic water sources with a variety of ecological benefits. However, trajectory Land Cover and Use Changes (LULC-C change poses significant threat to areas, negatively affecting quality. Thus, adoption remote sensing data Machine Learning Algorithms (MLAs) is novel approach that provides spatiotemporal on environmental changes resulting from LULC dynamics. Hence, this work harnessed Landsat imageries Random Forests (RF) classification as well hybrid model Multi-Layer Perceptron Markov chain (MLPNN-Markov) detect in forecast future changes. At every 5 years interval, RF generated more accurate maps for 2003–2023. The prediction 2019 also produced acceptable values kappa accuracy matrices, which were 65.50%, 58.4%, 90.90%, 0.52 overall accuracy, location, histogram, overall, respectively. findings highlighted decline forest strong negative correlation built-up mining areas. secondary invasion abandoned cropland occupied by grassland members was observed. displayed increasing trends between 2023. Wetlands water, however, exhibited steady trend minor variations. On other hand, each these persisted future, exception scaling-down behaviour 2032. outcomes will offer piece updated information LULC-C hints at possible direction This crucial local bodies tasked protect integrity aim improving

Language: Английский

Citations

0
Significance of different probability distributions in flood frequency analysis of Brahmani-Baitarani River Basin, India DOI Creative Commons

Robindro Singh Khwairakpam,

Sananda Kundu

Discover Geoscience, Journal Year: 2024, Volume and Issue: 2(1)

Published: Oct. 3, 2024

Language: Английский

Citations

3
Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application DOI
J. K. S. Sankalpa, A. M. R. W. S. D. Rathnayaka, P. G. N. Ishani

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(11)

Published: Oct. 8, 2024

Language: Английский

Citations

1
Comprehensive evaluation and future trend prediction of ecological security in Fuzhou City: a DIKW framework and multi-model integration analysis DOI

Shuhui Lai,

Xiaomei Li,

Jinming Sha

et al.

Human and Ecological Risk Assessment An International Journal, Journal Year: 2024, Volume and Issue: 30(9-10), P. 833 - 857

Published: Nov. 5, 2024

Understanding the ecological security situation of Fuzhou City holds significant theoretical and practical value for government departments in implementing development strategies achieving Sustainable Development Goal 11 (Sustainable Cities Communities). Using Data, Information, Knowledge, Wisdom (DIKW) framework, this study combined various remote sensing GIS methods to comprehensively analyze Fuzhou's past, present, future levels. The results showed a strong isotropic cluster city's security. Among influencing factors, degree regional was found have greatest impact, while water body coverage had least. factors are mutually reinforcing. Under natural scenario, area secure level 2020 decreased by 1243.70 km2, under protection it declined 1263.34 km2. In future, is expected face increasing fragmentation. Based on these findings, proposes balance economic City. These recommendations aim provide with relevant data support land resource management contribute high-level

Language: Английский

Citations

0
Spatiotemporal dynamics of land use land cover patterns in the middle Omo-Gibe River Basin, Ethiopia: machine learning, geospatial, and field survey integrated approach DOI
Abera Abiyo Dofee, Pritam Chand

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 31, 2024

Language: Английский

Citations

0