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27351 |
Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms
Enthalten in SN Computer Science Bd. 4, 6.5.2023, Nr. 4, date:7.2023: 1-22
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27352 |
Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
Enthalten in Theoretical and applied climatology Bd. 145, 20.5.2021, Nr. 1-2, date:7.2021: 673-686
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27353 |
Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review
Enthalten in Environmental monitoring and assessment Bd. 191, 5.3.2019, Nr. 4, date:4.2019: 1-28
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27354 |
Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis
Enthalten in Asian journal of atmospheric environment Bd. 18, 21.3.2024, Nr. 1, date:12.2024: 1-15
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27355 |
Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques
Enthalten in Scientific reports Bd. 13, 25.8.2023, Nr. 1, date:12.2023: 1-26
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27356 |
Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning
Enthalten in Environmental geochemistry and health Bd. 47, 26.12.2024, Nr. 2, date:2.2025: 1-22
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27357 |
Spatiotemporal analysis of land surface temperature and land cover changes in Prešov city using downscaling approach and machine learning algorithms
Enthalten in Environmental monitoring and assessment Bd. 197, 3.1.2025, Nr. 2, date:2.2025: 1-25
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27358 |
Spatiotemporal analysis of urban expansion, land use dynamics, and thermal characteristics in a rapidly growing megacity using remote sensing and machine learning techniques
Enthalten in Theoretical and applied climatology Bd. 156, 10.1.2025, Nr. 2, date:2.2025: 1-23
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27359 |
Spatiotemporal Changes of Pollutant Concentrations in South India during COVID-19 Lockdown Using Ground and Satellite-based data: a Comparative Analysis from the Machine Learning Model
Enthalten in Water, air & soil pollution Bd. 236, 28.2.2025, Nr. 3, date:3.2025: 1-25
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27360 |
Spatiotemporal characteristics of carbon emissions in Shaanxi, China, during 2012–2019: a machine learning method with multiple variables
Enthalten in Environmental science and pollution research Bd. 30, 10.7.2023, Nr. 37, date:8.2023: 87535-87548
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