Booth of the Lithographic Technical Foundation, Inc. at the Lithographic Technical Forum

This photo of the Lithographic Technical Forum shows the booth of the Lithographic Technical Foundation, Inc. The foundation was founded in 1924 to foster research and education in the lithography industry. The slogan "Research to improve lithography . . . Education to improve craft skill" can be seen at the very top of the booth. A group of people are depicted looking at the range of educational material about lithography on display. Black and white photograph.

Lithographic Technical Foundation, Inc. booth at the Lithographic Technical Forum

Three people are posed in front of the Lithographic Technical Foundation, Inc. booth at the Lithographic Technical Forum. The Lithographic Technical Foundation was founded in 1924 as an industry organization to promote research and education. In the 1960s the foundation changed its name to the Graphic Arts Technical Foundation and in the late 1990s merged with Printing Industry America. The organization ha a number of educational materials about lithography on display. Black and white photograph.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.