Learning Errors of Environmental Mathematical Models

Autor:	Dimitri Solomatine, Vadim Kuzmin, Durga Lal Shrestha
Rok vydání:	2013
Předmět:	Artificial neural network Mathematical model Computer science Stochastic process business.industry Extreme events Artificial intelligence business Machine learning computer.software_genre Unit-weighted regression computer Flooding (computer networking)
Zdroj:	Engineering Applications of Neural Networks ISBN: 9783642410123 EANN (1)
Popis:	In solving civil engineering problems the use of various models for forecasting environmental variables (for example, water levels in a river during flooding) is a must. Mathematical models of environmental processes inevitably contain errors (even if models are calibrated on accurate data) which can be represented as realizations of a stochastic process. Parameters of this process vary in time and cannot be reliably estimated without making (unrealistic) assumptions. However the model errors depend on various factors characterizing environmental conditions (for example, for extreme events errors are typically higher), and such dependencies can be reconstructed based on data. We present a unifying approach allowing for building machine learning models (in particular ANN and Local weighted regression) able to predict such errors as well as the properties of their distributions. Examples in modelling hydrological processes are considered.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::04f3c181e5610f594f5e1d1e7233ca21 https://doi.org/10.1007/978-3-642-41013-0_48 Zobrazit plný text záznamu