1985-1986 Yearbook

TOC: Life at LaGuardia…3/ LaGuardia At Work…11/College-Wide Activities…20/Mayor LaGuardia…26/Martin Luther King…28/Statue of Liberty…30/Activities and Trips…33/Recreation…52/Student Government and Clubs…56/Faculty and Staff…64/Letters to Graduates…83/Class ’85…87/Class ’86…115/Dedication to the Challenger…156/Boosters and Ads…158 Yearbook Committee: Project Director, VINCENT BANREY; Asst. Project Director, CATHY WHAN; Editor-in-Chief, MARGARET NEISS; Layout Editor, HORACIO OWENS; Asst. Layout Editor, MARICRUZ SAUNDERS; Copy Editor/Captain Editor, UMOJA KWANGUVU; Typesetter, EDWARD HOLLINS; Cover Artist, DAVID VAZQUEZ; End Sheets Photo, YOUNG BAEK CHOI; Finance Manager, GEORGE BERMUDEZ; Photographers: HORACIO OWENS, MARINA DIAZ, MARGARET NEISS, LORI GEORGE, RANDY FADER SMITH, UMOJA KWANGUVU, JUAN SEGARRA, PETER ABBATE, CLASSIC STUDIO; Production Staff: HORACIO OWENS, MARGARET NEISS, MARINA DIAZ, UMOJA KWANGUVU, MARICRUZ SAUNDERS, IRENE LEBRON, ARLENE BANREY, QUAALAN SAMUELS, MAYRA ALDONADO, CATHY WHAN, RAVI RAMDASS, GEORGE BERMUDEZ, BLANCA ARBITO, EDWARD HOLLINS, BRIDGET DAVIS; Feature Writers: YVONNE CANNON AND HARRIET ASCHOFF ("LaGuardia at Work"); GEORGE BERMUDEZ ("Mayor LaGuardia, A Civil Rights Political Leader"); SCOTT ENGEL ("Statue Of Liberty"); JEFFREY DAVIS ("Tribute to Ron Miller"); MARICRUZ SAUNDERS ("Challengers"); CASSANDRA WILLIAMS ("King: The Vision and the Fulfillment").

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.