Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

Karim Amininia; Seyed Mahdi Saghebian

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Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

机译：Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

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摘要

The flow assessment in a river is of vital interest in hydraulic engineering for flood warning andevacuation measures. To operate water structures more efficiently, models that forecast riverdischarge are desired to be of high precision and certain degree of accuracy. Therefore, in this study,two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariateadaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For thisaim, Mississippi river with three consecutive hydrometric stations was selected as case study. Usingthe previous MRF values during the period of 1950–2019, several models were developed and testedunder two scenarios (i.e. modeling based on station’s own data or previous station’s data). Wavelettransform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approacheswere used for enhancing modeling capability. Obtained results indicated that the integrated modelsresulted in more accurate outcomes. Data processing enhanced the model’s capability up to 25%.It was observed that the previous station’s data could be applied successfully for MRF modelingwhen the station’s own data were not available. The best-applied model dependability was assessedvia uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling.

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来源
《Journal of hydroinformatics》 |2021年第4期|897-913|共17页
作者
Karim Amininia; Seyed Mahdi Saghebian;
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作者单位

Department of Geography, Ahar Branch,Islamic Azad University,Ahar,Iran;

Department of Civil Engineering, Ahar Branch,Islamic Azad University,Ahar,Iran;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种英语
中图分类水文科学（水界物理学）;
关键词
consecutive stations; EMD; KELM; pre-processing; river discharge;

Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

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