About: GENERATING RAINFALL-RUNOFF DATA COLLECTION FOR CALIBRATION OF MACHINE LEARNING DRIVEN MODELS

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Machine learning driven models provide a useful alternative for analytic modeling software in many domains. Simulating the rainfall-runoff process (i.e. transforming the fallen precipitations into the runoff in the corresponding outlet) is no exception and there are a lot of machine learning alternatives such as case-based reasoning, artificial neural networks etc. To facilitate their proper function, it is necessary to correctly set up the algorithm parameters or to provide a meaningful training data collection. However, in some areas, where the floods are not very frequent, it can be almost impossible to obtain the required combination of input measured precipitations amount and the corresponding measured output discharge in the outlet. In such case, the utilization of analytic modeling software (such as HEC-HMS, MIKESHE, HBV etc.) can be very helpful. This paper describes in detail our procedure for generating desirable data collection using such software including distorting of inputs and concatenation of partial results. It also clarifies the usage of verified rainfall-runoff model (the Floreon+ system) and selection of studied area (Odra catchment). Machine learning driven models provide a useful alternative for analytic modeling software in many domains. Simulating the rainfall-runoff process (i.e. transforming the fallen precipitations into the runoff in the corresponding outlet) is no exception and there are a lot of machine learning alternatives such as case-based reasoning, artificial neural networks etc. To facilitate their proper function, it is necessary to correctly set up the algorithm parameters or to provide a meaningful training data collection. However, in some areas, where the floods are not very frequent, it can be almost impossible to obtain the required combination of input measured precipitations amount and the corresponding measured output discharge in the outlet. In such case, the utilization of analytic modeling software (such as HEC-HMS, MIKESHE, HBV etc.) can be very helpful. This paper describes in detail our procedure for generating desirable data collection using such software including distorting of inputs and concatenation of partial results. It also clarifies the usage of verified rainfall-runoff model (the Floreon+ system) and selection of studied area (Odra catchment). (en)
Title	GENERATING RAINFALL-RUNOFF DATA COLLECTION FOR CALIBRATION OF MACHINE LEARNING DRIVEN MODELS GENERATING RAINFALL-RUNOFF DATA COLLECTION FOR CALIBRATION OF MACHINE LEARNING DRIVEN MODELS (en)
skos:prefLabel	GENERATING RAINFALL-RUNOFF DATA COLLECTION FOR CALIBRATION OF MACHINE LEARNING DRIVEN MODELS GENERATING RAINFALL-RUNOFF DATA COLLECTION FOR CALIBRATION OF MACHINE LEARNING DRIVEN MODELS (en)
skos:notation	RIV/61989100:27740/14:86091971!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), P(EE2.3.30.0055)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Fedorčák, Dušan Kocyan, Tomáš Martinovič, Jan Podhorányi, Michal
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / IT4Innovations
http://linked.open...dnocenehoVysledku	18048
http://linked.open...ai/riv/idVysledku	RIV/61989100:27740/14:86091971
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	machine learning, time series, rainfall-runoff model, training data, floods (en)
http://linked.open.../riv/klicoveSlovo	training data rainfall-runoff model time series floods machine learning
http://linked.open...ontrolniKodProRIV	[D3E8B6FF2A36]
http://linked.open...v/mistoKonaniAkce	Albena
http://linked.open...i/riv/mistoVydani	Sofia
http://linked.open...i/riv/nazevZdroje	SGEM 2014: 14th GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING Conference proceedings Volume II
http://linked.open...in/vavai/riv/obor	DA
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	New creative teams in priorities of scientific research IT4Innovations Centre of Excellence
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Kocyan, Tomáš Martinovič, Jan Podhorányi, Michal Fedorčák, Dušan
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-06-17 (xsd:date)
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.5593/SGEM2014/B21/S7.025
http://purl.org/ne...btex#hasPublisher	STEF92 Technology Ltd.
https://schema.org/isbn	978-619-7105-10-0
http://localhost/t...ganizacniJednotka	27740

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