Results 211 to 220 of about 3,437,660 (362)

Self-supervised and few-shot learning for robust bioaerosol monitoring. [PDF]

Aerobiologia (Bologna)
Willi A, Baumann P, Erb S, Gröger F, Zeder Y, Lionetti S.   +5 more
europepmc   +1 more source

Using the Amazing atmosphere to Foster Student Learning and Interest in Meteorology

, 2012
B. Barrett, John E Woods
semanticscholar   +1 more source

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source

Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology. [PDF]

Nat Commun
Jennings KS, Collins M, Hatchett BJ, Heggli A, Hur N, Tonino S, Nolin AW, Yu G, Zhang W, Arienzo MM.   +9 more
europepmc   +1 more source

Dynamic Tracking: A Machine Learning Approach for PV Yield Optimization

Solar RRL, EarlyView.
A new method for photovoltaic yield optimization through machine learning‐accelerated irradiance prediction is presented. This framework combines ray‐tracing accuracy with neural network speed, enabling real‐time simulation of all possible tilt configurations considering diffuse and direct irradiation.
Daniel Burkhardt, Maddalena Bruno, Leslie Kurumundayil, Matthew Berwind, Matthias Demant   +4 more
wiley   +1 more source

A novel approach to predict the arctic stratospheric ozone from stratospheric polar vortex dynamics using explainable machine learning. [PDF]

Sci Rep
Kumar A, Mandal J, Mehrdad S, Jacobi C.
europepmc   +1 more source

A Probabilistic Approach to Quantify the Uncertainty of the 1 Min Irradiance Estimates from the Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service

Solar RRL, EarlyView.
This paper presents the development and evaluation of a probabilistic uncertainty model for the CAMS radiation service irradiance estimates. This model is based on the conditioning of the cumulative distribution function of the deviations to the model inputs.
Jorge Enrique Lezaca Galeano, Yves‐Marie Saint‐Drenan, Marion Schroedter‐Homscheidt   +2 more
wiley   +1 more source

Species Richness of Freshwater Fish Trophic Guilds Increases With Tropical River Discharge and Decreases With Variability. [PDF]

Ecol Evol
Perna CN, Sternberg D, Kennard MJ, Luiz OJ, Irvine DJ, Stratford D, Kopf RK.   +6 more
europepmc   +1 more source

Hybrid Data‐Driven Modeling and Prediction of Photovoltaic Soiling Losses: Balancing Accuracy and Simplicity

Solar RRL, EarlyView.
This study introduces a hybrid data‐driven framework for modeling and predicting soiling losses in PV plants, coupling a statistical quantification approach (stochastic quantifying soiling loss) with machine learning models. The framework balances simplicity and accuracy: it works reliably in data‐scarce sites while exploiting environmental datasets ...
Ioannis (John) A. Tsanakas, E. Pilat, S. Arbaretaz, M.‐T. Doi, E. Planès, F. Monteiro Martins, J. Veludo, C. Ménézo   +7 more
wiley   +1 more source

Understanding ozone variability in spatial responses to emissions and meteorology in China using interpretable machine learning. [PDF]

iScience
Zhang X, Zhang WC, Wu W, Liu HB.
europepmc   +1 more source