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"Machine Learning"
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27741
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
27742
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
27743
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
27744
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
27745
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
27746
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.
27747
Spatiotemporal modelling of rainfall-induced landslides using machine learning
Enthalten in Landslides Bd. 18, 15.4.2021, Nr. 7, date:7.2021: 2499-2514
27748
Spatiotemporal trends and meteorological influence on hand, foot, and mouth disease in Ho Chi Minh City: insights from 2015–2022 using machine learning and trend analysis
Enthalten in Discover public health Bd. 22, 11.6.2025, Nr. 1, date:12.2025: 1-14
27749
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
27750
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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