Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(21), С. 30370 - 30398

Опубликована: Апрель 20, 2024

Язык: Английский

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Analyzing trade-offs, synergies, and driving factors of ecosystem services in Anhui Province using spatial analysis and XG-boost modeling

Ecological Indicators, Год журнала: 2025, Номер 171, С. 113098 - 113098

Опубликована: Янв. 24, 2025

Язык: Английский

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Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105654 - 105654

Опубликована: Июль 9, 2024

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly rapidly developing regions with harsh climates. This study examines the seasonal variation of Doha, Qatar, exploring interconnectedness land use/land cover (LULC) socio-demographic characteristics household consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation Bootstrap analysis) advanced geostatistical models, including Geographically Weighted Regression (GWR) Multiscale (MGWR), to analyze monitor spatial variations The methods involved assessing relationship between surface temperature (LST), water-electricity consumption, analyzing impact demographic variables. Key findings indicate significant spatiotemporal influenced by changes LULC such as size structure. highlight need for integrated planning energy policies that consider impacts enhance efficiency sustainability settings. Furthermore, results underscore importance addressing complex interplay development resource policy-making.

Язык: Английский

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High-Resolution Insights into Nighttime Urban Heat Island Detection: a comparative temporal analysis of 1988 and 2015 in Greater Cairo

GeoJournal, Год журнала: 2025, Номер 90(1)

Опубликована: Янв. 23, 2025

Язык: Английский

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A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Фев. 4, 2025

Язык: Английский

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning

Sensors, Год журнала: 2025, Номер 25(4), С. 1169 - 1169

Опубликована: Фев. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Язык: Английский

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Detecting the Changing Impact of Urbanisation on Urban Heat Islands in a Tropical Megacity Using Local Climate Zones

Energy and Built Environment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Machine Learning in Modeling Urban Heat Islands: A Data-Driven Approach for Kuala Lumpur

Earth Systems and Environment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Язык: Английский

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Spatio-temporal Analysis of LST, NDVI and SUHI in a Coastal Temperate City using Local Climate Zone

Energy and Built Environment, Год журнала: 2024, Номер unknown

Опубликована: Июнь 1, 2024

Extreme heat due to changing climate poses a new challenge for temperate climates. The is further aggravated by inadequate research, policy, or preparedness effectively respond and recover from its impacts. While urban morphology plays crucial role in mitigating heat, it has received limited attention planning, highlighting the need exploration, particularly regions. To illustrate potential mitigations, we use example of coastal city Cardiff. establish interrelations between island patterns, explored spatiotemporal variations land surface temperature (LST), normalised difference vegetation index (NDVI), (SUHI) local zone (LCZ) classification Results showed significant variation SUHI LCZ zones. Both LST NDVI were found vary significantly across zones demonstrating their association with form locality. For built-up areas, more compact built-environment smaller cover larger building density was 2.0°C warmer than open when comparing mean summer LSTs. On average, natural classes exhibit that 8.0°C lower 6.0°C built-environment. Consequently, high-density, LCZs have greater effect compared classes. Therefore, cities will benefit incorporating an sufficient greenery spaces. These findings help determine optimal climates develop mitigation strategies while designing, improving existing areas. In addition, map applied this study Cardiff enable international comparison testing proven change adaptation techniques similar

Язык: Английский

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Hourly impact of urban features on the spatial distribution of land surface temperature: A study across 30 cities

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105701 - 105701

Опубликована: Июль 27, 2024

Язык: Английский

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