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27011 - 27020 von 32640

	27011	Mapping human security strategies to sustainable development goals: a machine learning approach Enthalten in Discover sustainability Bd. 6, 13.2.2025, Nr. 1, date:12.2025: 1-17
	27012	Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective Enthalten in Environmental earth sciences Bd. 84, 30.4.2025, Nr. 9, date:5.2025: 1-20
	27013	Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique Enthalten in The European journal of health economics Bd. 22, 13.1.2021, Nr. 2, date:3.2021: 341-350
	27014	Mapping mangroves land suitability using machine learning and MCDM in the Indus Delta, Pakistan Enthalten in Theoretical and applied climatology Bd. 156, 19.6.2025, Nr. 7, date:7.2025: 1-20
	27015	Mapping natural fracture networks using geomechanical inferences from machine learning approaches Enthalten in Computational geosciences Bd. 26, 12.3.2022, Nr. 3, date:6.2022: 651-676
	27016	Mapping of 10-km daily diffuse solar radiation across China from reanalysis data and a Machine-Learning method Enthalten in Scientific data Bd. 11, 11.7.2024, Nr. 1, date:12.2024: 1-13
	27017	Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods Enthalten in Moskovskij gosudarstvennyj universitet im. M.V. Lomonosova: Moscow University soil science bulletin Bd. 79, 18.12.2024, Nr. 4, date:12.2024: 500-508
	27018	Mapping of Debris-Covered Glaciers Using Object-Based Machine Learning Technique Enthalten in Indian Society of Remote Sensing: Journal of the Indian Society of Remote Sensing Bd. 52, 24.2.2024, Nr. 2, date:2.2024: 399-411
	27019	Mapping of high-resolution daily particulate matter (PM2.5) concentration at the city level through a machine learning-based downscaling approach Enthalten in Environmental monitoring and assessment Bd. 197, 23.12.2024, Nr. 1, date:1.2025: 1-22
	27020	Mapping of soil suitability for medicinal plants using machine learning methods Enthalten in Scientific reports Bd. 14, 14.2.2024, Nr. 1, date:12.2024: 1-16

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