IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 35th IAHR Congress - Chengdu (2013) : THEME 5 - FLUVIAL HYDRAULICS AND RIVER MANAGEMENT : Development of Discharge-Stage Curves Using Artificial Neural Networks and Model Trees
Development of Discharge-Stage Curves Using Artificial Neural Networks and Model Trees
Author : Vincent Wolfs and Patrick Willems
Reliable discharge data from rivers forms a vital source of information in hydrological practices. To obviate the need of expensive and difficult to conduct discharge measurements, hydrologists often turn to the use of rating curves in combination with stage measurements. Usually, regression techniques are employed to determine the rating curve, based on historical flow and stage data. Because of the problem complexity, these techniques do not always provide an unambiguous relationship between stage and discharge. This study aims to overcome this issue by comparing multiple approaches on an hourly set of stage-discharge couples, obtained via simulations with a detailed full one-dimensional hydrodynamic model of the Belgian river Demer. Firstly, the conventional rating curve methodology is applied. Next, the use of expert systems is examined, allowing for a more flexible model structure identification and calibration. The performance of artificial neural networks is compared to that of M5? model trees.Results show that due to the expert systems? flexibility, they manage to outperform the conventional rating curve, albeit only slightly. However, as demonstrated in this paper, this kind of models is prone to overfitting. Even with a carefully calibration procedure, the models can become overparameterized if the amount of data presented during training is too small, or if the data does not cover all possible conditions. Secondly, close inspection of the appropriate input space is required. This study presents autoregressive models that perform excellent in one-step ahead prediction, but fail when previously calculated values are used as input.
File Size : 496,106 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 35th IAHR Congress - Chengdu (2013)
Date Published : 18/07/2016
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