A decision framework for potential dam site selection using GIS, MIF and TOPSIS in Ulhas river basin, India

Journal of Cleaner Production, Год журнала: 2023, Номер 423, С. 138890 - 138890

Опубликована: Сен. 14, 2023

Язык: Английский

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Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

Urban Climate, Год журнала: 2024, Номер 55, С. 101962 - 101962

Опубликована: Май 1, 2024

Язык: Английский

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Impacts of climate factors and human activities on NDVI change in China

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102555 - 102555

Опубликована: Март 18, 2024

Vegetation plays a crucial role in terrestrial ecosystems, and there has been substantial shift global vegetation cover recent decades. China is recognized for its impact on changes, which are influenced by both climate change human activities. Therefore, this research aims to assess the respective influences of modification activities variations China. First, changes explored between 1982 2020 using satellite-image derived index, known as Normalized Difference Index (NDVI). Second, multiple regression model based time-lag analysis used simulate NDVI. In addition common climatic factors such temperature, precipitation, solar radiation intensity relative humidity, atmospheric CO2 concentration directly reflect considered model. Finally, influence variation alteration determined reconstructed Results: (1) Precipitation most important influences, while carbon dioxide humidity have least influence. (2) The simulation error before 2000 was 0.875%, considerably lower than after 2000. (3) After 2000, favorably affected recovery study area, with an average degree >30%.

Язык: Английский

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Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141035 - 141035

Опубликована: Фев. 8, 2024

Язык: Английский

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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Апрель 24, 2024

Abstract Land use and land cover (LULC) analysis is crucial for understanding societal development assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing under cloud limited ground truth data. To enhance accuracy comprehensiveness of descriptions changes, this investigation employed a combination advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, addition to computing capabilities Google Earth Engine (GEE) platform. Additionally, study incorporated random forest (RF) algorithm. This aimed generate continuous maps 2014 2020 Shrirampur area Maharashtra, India. A novel multiple composite RF approach based on classification utilized final utilizing RF-50 RF-100 tree models. Both models seven input bands (B1 B7) as dataset classification. By incorporating these bands, were able influence spectral information captured by each band classify categories accurately. The inclusion enhanced discrimination classifiers, increasing assessment classes. indicated that exhibited higher training validation/testing (0.99 0.79/0.80, respectively). further revealed agricultural land, built-up water bodies have changed adequately undergone substantial variation among classes area. Overall, research provides insights into application machine learning (ML) emphasizes importance selecting optimal enhancing reliability GEE different present enabled interpretation pixel-level interactions while improving image suggested best through identification

Язык: Английский

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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 24, 2025

The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for planners decision-makers. current research was carried out to study use cover (LULC) changes associated LST patterns planned city (Kabul) unplanned (Jalalabad), Afghanistan, using Support Vector Machine (SVM) Landsat data from 1998 2018. Future LULC were predicted 2028 2038 Cellular Automata-Markov (CA-Markov) Artificial Neural Network (ANN) models. results clearly emphasize different between Kabul Jalalabad. Between 2018, built-up areas Jalalabad increased by 16% 30%, respectively, while bare soil vegetation decreased 15% 1% 4% 30% showed highest seasonal annual LST, followed vegetation. maximum occurred during summer both cities predictions that (48% 55% 2018) will increase approximately 59% 68% 79% Jalalabad, respectively. Similarly, simulations percentage with higher (> 35°C) would (0% 5% 22% 43% 2038, Kabul's shows lower than Jalalabad's city, primarily due urbanization greater center. Urban should limit development reduce potential impacts high temperatures.

Язык: Английский

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The spatial impact of population semi-urbanization on sulfur dioxide emissions: Empirical evidence from China

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124257 - 124257

Опубликована: Янв. 25, 2025

Язык: Английский

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Evaluating urban climate resilience in the Yangtze River Delta urban agglomeration: A novel method integrating the DPSIR model and Sustainable Development Goals

Journal of Environmental Management, Год журнала: 2025, Номер 376, С. 124517 - 124517

Опубликована: Фев. 18, 2025

Язык: Английский

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Identification of sustainable urban settlement sites using interrelationship based multi-influencing factor technique and GIS

Geocarto International, Год журнала: 2023, Номер 38(1)

Опубликована: Окт. 17, 2023

Evaluation of a site suitability for sustainable urban settlement growth is crucial. A system evaluating an interaction between the factors must be established to assure that critical selection interrelationships are not neglected. To solve this problem, proposed methodology was evaluated identify suitable residential development in Nashik, India. The Geographic information (GIS) -based multi-influence factor (MIF) approach used study find ideal locations future settlement. assessment based on 11 including vegetation, elevation, land use, industry, drainage network, slope, water bodies, road, health services, railway station, and population density. delineated by aggregating all considered their associated MIF weights using interrelationship factors. results showed 27.26% research area development, 16.82% low suitable, 30.65% moderately 16.48% highly 8.77% very suitable. Most sites located near existing habitant area, major roads. were validated Receiver-Operating Characteristic (ROC), Area Under Curve (AUC) value 0.895 indicated model effective. Sensitivity analysis revealed distance from services dominant selecting optimal location area. findings areas intensive will helpful planners policy makers future.

Язык: Английский

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Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Journal of Human Earth and Future, Год журнала: 2024, Номер 5(2), С. 216 - 242

Опубликована: Июнь 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Язык: Английский

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