Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks

Wolfs, Vincent; Willems, Patrick

doi:10.1016/j.envsoft.2014.01.021

Cited by 46 publications

(20 citation statements)

References 44 publications

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“…Regarding area-stage relationships, clockwise hysteresis is reported for river floodplain systems, as some water remains in local low-lying zones during the water-falling period, producing an area larger than that of the water-rising period (e.g., [11]). With regard to discharge-stage relationships caused by flooding, counter-clockwise hysteresis appears due to an additional surface gradient originating from flooding occurring during the flood rising period [14]. However, for discharge-stage relationships controlled by backwater effects, different directions can manifest depending on the effects of backwater.…”

Section: Discussionmentioning

confidence: 99%

“…Phillips et al found hysteresis between runoff connectivity and streamflow in the Baker Creek Research Basin [13]. Wolfs et al used three approaches to develop discharge-stage curves affected by hysteresis [14]. The corresponding results show that all of the methods used are superior to the traditional rating curve, and that the model tree method is the best because it is easy to understand and generates accurate outputs.…”

Section: Et Al Adopted Moderate Resolutionmentioning

confidence: 99%

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Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

Huang

Peng

Liu

et al. 2017

Water

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Flood dynamics of large lake floodplain systems are typically complex. This paper analyses the characteristics and factors that influence the hysteresis of water area-stage curves for Poyang Lake, the largest freshwater lake in China characterized by complex geomorphology and upstream-downstream exchange conditions. For this purpose, a two-dimensional hydrodynamic model (EFDC) based on seven scenarios is established. The results indicate that the area-stage curve presents significant hysteretic characteristics due to different water surface gradients that emerge during the water-rising and water-falling periods. Counter-clockwise, clockwise, and splayed hysteresis directions observed at the northern, southern, and central hydrometric stations, respectively, are found in Poyang Lake for the first time. Upstream catchment inflows and Hukou stage reflecting the downstream condition are the main factors that influence hysteresis. The temporal fluctuation of catchment inflows and Hukou stage has a remarkably positive impact on hysteresis, namely, an increase in fluctuation brings about a larger hysteresis. The effects of magnitude change in the two factors on hysteresis are opposing. Catchment inflows are positively related, while the decline of the Hukou stage will produce a more pronounced hysteresis. The outcomes of this study will benefit the water management of Poyang Lake and other similar large lakes.

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Section: Discussionmentioning

confidence: 99%

Section: Et Al Adopted Moderate Resolutionmentioning

confidence: 99%

Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

Huang

Peng

Liu

et al. 2017

Water

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“…Numerous eager learning models have been developed to solve various problems. Since the resurgence of ANNs in the late 1980s (begun by the introduction of the backpropagation training algorithm for feedforward ANNs), ANNs have become the preferred prediction approach, applied widely in flow routing and river-stage forecasting (e.g., Liong et al, 2000;Maier and Dandy, 2000;Kerh and Lee, 2006;Altunkaynak, 2007;Ondimu and Murase, 2007;Lin et al, 2010;Alvisi and Franchini, 2011;Tsai et al, 2012;Wolfs and Willems, 2014). The value of ANNs is that feedforward networks (such as multilayer perceptrons, MLP) are universal approximators and can learn any continuous functions with arbitrary accuracy.…”

Section: Introductionmentioning

confidence: 99%

Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

Wei

2015

Environmental Modelling & Software

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“…During the training process with a backpropagation algorithm, the output errors are repeatedly fed back into the network to adjust connection weights and biases until optimal values are obtained [40]. The number of hidden neurons and the number of hidden layers is often determined by trial and error [41,42]. In this study, one or two hidden layers with a number of neurons between two and twenty are considered.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain

Jimeno‐Sáez

Senent‐Aparicio

Pérez‐Sánchez

et al. 2017

Water

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Abstract:The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the consequences derived from flash floods, especially in Mediterranean countries. In this study, empirical methods to estimate the IPF based on maximum mean daily flow (MMDF), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) have been compared. These methods have been applied in 14 different streamflow gauge stations covering the diversity of flashiness conditions found in Peninsular Spain. Root-mean-square error (RMSE), and coefficient of determination (R 2 ) have been used as evaluation criteria. The results show that: (1) the Fuller equation and its regionalization is more accurate and has lower error compared with other empirical methods; and (2) ANFIS has demonstrated a superior ability to estimate IPF compared to any empirical formula.

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Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks

Cited by 46 publications

References 44 publications

Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain

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