1 resultado para predictive models
em CUNY Academic Works
Filtro por publicador
- Abertay Research Collections - Abertay University’s repository (1)
- Aberystwyth University Repository - Reino Unido (2)
- Adam Mickiewicz University Repository (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- Aquatic Commons (9)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (12)
- Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux: (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (14)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (5)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (3)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (11)
- Brock University, Canada (1)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (17)
- CentAUR: Central Archive University of Reading - UK (30)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (2)
- Coffee Science - Universidade Federal de Lavras (1)
- Collection Of Biostatistics Research Archive (2)
- CORA - Cork Open Research Archive - University College Cork - Ireland (4)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (2)
- Deakin Research Online - Australia (53)
- Digital Commons - Michigan Tech (6)
- Digital Commons at Florida International University (9)
- Digital Knowledge Repository of Central Drug Research Institute (1)
- DigitalCommons@The Texas Medical Center (8)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (2)
- Duke University (6)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (9)
- FAUBA DIGITAL: Repositorio institucional científico y académico de la Facultad de Agronomia de la Universidad de Buenos Aires (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (4)
- Helda - Digital Repository of University of Helsinki (3)
- Indian Institute of Science - Bangalore - Índia (6)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Superior de Psicologia Aplicada - Lisboa (1)
- Massachusetts Institute of Technology (1)
- Memorial University Research Repository (1)
- National Center for Biotechnology Information - NCBI (2)
- Nottingham eTheses (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Publishing Network for Geoscientific & Environmental Data (11)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (23)
- Queensland University of Technology - ePrints Archive (540)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (3)
- Repositório Científico da Universidade de Évora - Portugal (7)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (4)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (2)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional da Universidade de Brasília (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (17)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- SAPIENTIA - Universidade do Algarve - Portugal (4)
- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (17)
- Universidade Complutense de Madrid (1)
- Universidade de Madeira (1)
- Universidade dos Açores - Portugal (1)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (4)
- Universidade Metodista de São Paulo (3)
- Universitat de Girona, Spain (4)
- Université de Lausanne, Switzerland (2)
- Université de Montréal (1)
- Université de Montréal, Canada (10)
- Université Laval Mémoires et thèses électroniques (2)
- University of Queensland eSpace - Australia (9)
- University of Southampton, United Kingdom (2)
- University of Washington (4)
- WestminsterResearch - UK (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
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.