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Ergebnis der Suche nach: "Machine Learning"
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371 - 380 von 32527

	371	Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events Enthalten in Atmospheric chemistry and physics Bd. 25, 2025, Nr. 13: 7431-7446. 16 S.
	372	Machine learning for automated avalanche terrain exposure scale (ATES) classification Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 11: 4375-4403. 29 S.
	373	Hydrological drought prediction and its influencing features analysis based on a machine learning model Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 11: 4299-4316. 18 S.
	374	Future intensification of compound and consecutive drought and heatwave risks in Europe Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 10: 3879-3895. 17 S.
	375	Predictive understanding of socioeconomic flood impact in data-scarce regions based on channel properties and storm characteristics: application in High Mountain Asia (HMA) Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 10: 3759-3778. 20 S.
	376	Research on the extraction of pre-seismic anomalies in borehole strain data of the Maduo earthquake based on the SVMD-Informer model Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 9: 3603-3618. 16 S.
	377	Predicting Soil Salinity in the Red River Delta (Vietnam) Using Machine Learning and Assessing Farmers' Adaptive Capacity Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 9: 3505-3524. 20 S.
	378	Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 9: 3333-3353. 21 S.
	379	Evaluating Yangtze River Delta Urban Agglomeration flood risk using a hybrid method of automated machine learning and analytic hierarchy process Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 9: 3087-3108. 22 S.
	380	Reask UTC: a machine learning modeling framework to generate climate-connected tropical cyclone event sets globally Enthalten in Natural hazards and earth system sciences Bd. 25, 2025, Nr. 8: 2863-2884. 22 S.

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