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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A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Апрель 10, 2025

Язык: Английский

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Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models

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

Опубликована: Фев. 27, 2025

Urban expansion and changes in land use/land cover (LULC) have intensified recent decades due to human activity, influencing ecological developmental landscapes. This study investigated historical projected LULC urban growth patterns the districts of Multan Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, predictive modelling from 1990 2030. The analysis images was grouped into four time periods (1990–2000, 2000–2010, 2010–2020, 2020–2030). Google Earth Engine cloud-based platform facilitated classification 5 ETM (1990, 2000, 2010) 8 OLI (2020) Random Forest model. A simulation model integrating Cellular Automata an Artificial Neural Network Multilayer Perceptron MOLUSCE plugin QGIS employed forecast resulting maps showed consistently high accuracy levels exceeding 92% for both across all periods. revealed that Multan's built-up area increased 240.56 km2 (6.58%) 440.30 (12.04%) 2020, while Sargodha experienced more dramatic 730.91 (12.69%) 1,029.07 (17.83%). Vegetation remained dominant but significant variations, particularly peri-urban areas. By 2030, is stabilize at 433.22 km2, primarily expanding southeastern direction. expected reach 1,404.97 showing balanced multi-directional toward northeast north. presents effective analytical method processing, GIS, change modeling evaluate spatiotemporal changes. approach successfully identified main transformations trends areas highlighting potential urbanization zones where opportunities exist developing planned managed settlements.

Язык: Английский

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Modeling and Forecasting Monthly Humidity in South Asia: A SARIMA Approach

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

Опубликована: Март 8, 2025

Язык: Английский

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Impacts of tourism on LULC and LST dynamics in district Buner and Shangla, Pakistan

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

Опубликована: Март 18, 2025

Tourism activities are changing the global landscape pattern. This study attempted to estimate changes in Land Use Cover (LULC) and Surface Temperature (LST) District Buner Shangla, Khyber Pakhtunkhwa (KPK), Pakistan, specifically its tourist spots. Using remote sensing data from satellites (1990–2020) future projections (2035–2050), we applied Artificial Neural Network (ANN) Cellular Automata Markov (CA-Markov) models examine past LULC LST dynamics across two districts including four major spots (Shangla Top as spot one (TS1), Bar Puran (TS2), Shahida Sar (TS3), Daggar (TS4). The classification for whole area indicates that built-up agricultural areas increased with a net change of +0.8% +3.2% Shangla districts, respectively. highest mean was found areas. simulation results indicate an expansion 4.5% 5.8% total areas, above 31 °C will cover 76% 88% 2035 2050, conversion is driven by tourism activities, causing urban heat island effects (UHIs), environmental degradation. analysis shows at while (28 °C) (2035–2050) show TS4 would have (5.67%), (31 65.23 82.20%. These findings provide essential understandings developing long-term policies meant moderate impact region.

Язык: Английский

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Integrating spatial analysis of land surface temperature and green space suitability: an advanced approach to urban and peri-urban planning

GeoJournal, Год журнала: 2025, Номер 90(2)

Опубликована: Март 18, 2025

Язык: Английский

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Evaluating the impact of urbanization patterns on LST and UHI effect in Afghanistan’s Cities: a machine learning approach for sustainable urban planning

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Май 5, 2025

Язык: Английский

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