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32451 - 32460 von 32569

	32451	Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones for drinking activities Enthalten in Discover civil engineering Bd. 2, 1.8.2025, Nr. 1, date:12.2025: 1-53
	32452	Water quality monitoring and management: integration of machine learning algorithms and Sentinel-2 images for the estimation of Chlorophyll-a Enthalten in Modeling earth systems and environment Bd. 11, 19.7.2025, Nr. 5, date:10.2025: 1-26
	32453	Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake Enthalten in Environmental monitoring and assessment Bd. 197, 15.4.2025, Nr. 5, date:5.2025: 1-17
	32454	Water Quality Prediction Method Coupling Mechanism Model and Machine Learning for Water Diversion Projects with a Lack of Data Enthalten in Water resources management Bd. 39, 24.3.2025, Nr. 7, date:5.2025: 3015-3030
	32455	Water-saving control system based on multiple intelligent algorithms Enthalten in Autonomous intelligent systems Bd. 4, 4.7.2024, Nr. 1, date:12.2024: 1-9
	32456	Watershed prioritization and decision-making based on weighted sum analysis, feature ranking, and machine learning techniques Enthalten in Arabian journal of geosciences Bd. 16, 6.1.2023, Nr. 1, date:1.2023: 1-20
	32457	Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy Enthalten in Nature Communications Bd. 13, 5.7.2022, Nr. 1, date:12.2022: 1-12
	32458	Wavelet scattering and scalogram visualization based human brain decoding using empirical wavelet transform Enthalten in International journal of information technology Bd. 15, 9.3.2023, Nr. 3, date:3.2023: 1699-1708
	32459	Wavelet $$s$$ -Wasserstein distances for $$0 < s\leqslant\,1$$ Enthalten in Sampling theory, signal processing, and data analysis Bd. 23, 6.8.2025, Nr. 2, date:12.2025: 1-48
	32460	Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements Enthalten in Nature Communications Bd. 13, 7.9.2022, Nr. 1, date:12.2022: 1-3

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