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26601 |
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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26602 |
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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26603 |
Spatiotemporal dynamics of land use land cover patterns in the middle Omo-Gibe River Basin, Ethiopia: machine learning, geospatial, and field survey integrated approach
Enthalten in Modeling earth systems and environment Bd. 11, 31.12.2024, Nr. 1, date:2.2025: 1-22
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26604 |
Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development
Enthalten in Environmental monitoring and assessment Bd. 191, 23.11.2019, Nr. 12, date:12.2019: 1-23
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26605 |
Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors
Enthalten in Earth science informatics Bd. 18, 8.1.2025, Nr. 1, date:1.2025: 1-23
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26606 |
Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics
Enthalten in Research on biomedical engineering Bd. 38, 17.2.2022, Nr. 2, date:6.2022: 499-537
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26607 |
Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-3-2024, 2024: 371-375. 5 S.
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26608 |
Spatiotemporal modelling of rainfall-induced landslides using machine learning
Enthalten in Landslides Bd. 18, 15.4.2021, Nr. 7, date:7.2021: 2499-2514
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26609 |
Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data
Enthalten in Environmental management Bd. 75, 7.1.2025, Nr. 3, date:3.2025: 694-709
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26610 |
Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images
Enthalten in Scientific reports Bd. 15, 21.1.2025, Nr. 1, date:12.2025: 1-15
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