Artificial Neural Network Concepts in Hydrology

Minns, A. W.; Hall, Michael J.

doi:10.1002/0470848944.hsa018

Cited by 8 publications

(12 citation statements)

References 88 publications

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“…As proposed in the recent literature (Kocjancic and Zupan, 2001;Bowden et al, 2002;Shahin et al, 2004) a self-organising map (SOM) may be applied to this aim. The SOM is a data-driven classification method based on unsupervised artificial neural networks that may be applied for several clustering purposes (for hydrological applications see, for example, Minns and Hall, 2005;Kalteh et al, 2008).…”

Section: Identification Of Balanced Cross-validation Subsets With Sommentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

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Abstract. In many real-world flood forecasting systems, the runoff thresholds for activating warnings or mitigation measures correspond to the flow peaks with a given return period (often 2 years, which may be associated with the bankfull discharge). At locations where the historical streamflow records are absent or very limited, the threshold can be estimated with regionally derived empirical relationships between catchment descriptors and the desired flood quantile. Whatever the function form, such models are generally parameterised by minimising the mean square error, which assigns equal importance to overprediction or underprediction errors.Considering that the consequences of an overestimated warning threshold (leading to the risk of missing alarms) generally have a much lower level of acceptance than those of an underestimated threshold (leading to the issuance of false alarms), the present work proposes to parameterise the regression model through an asymmetric error function, which penalises the overpredictions more.The estimates by models (feedforward neural networks) with increasing degree of asymmetry are compared with those of a traditional, symmetrically trained network, in a rigorous cross-validation experiment referred to a database of catchments covering the country of Italy. The analysis shows that the use of the asymmetric error function can substantially reduce the number and extent of overestimation errors, if compared to the use of the traditional square errors. Of course such reduction is at the expense of increasing underestimation errors, but the overall accurateness is still acceptable and the results illustrate the potential value of choosing an asymmetric error function when the consequences of missed alarms are more severe than those of false alarms.

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Section: Identification Of Balanced Cross-validation Subsets With Sommentioning

confidence: 99%

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Toth

2016

Hydrol. Earth Syst. Sci.

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“…Consequently, there is a need for systems capable of efficiently forecasting water levels or discharge rates in rivers. Previous work has been supportive of the neural networks for flow forecasting; (Imrie et al, 2000;Minns and Hall, 1996;Minns and Hall, 1997;Dawson and Wilby, 1998;Dawson and Wilby, 1999;Campolo et al, 1999;Liong et al, 2000). Artificial neural networks provide a fast and flexible means for developing non-linear flow routing models.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for flood forecasting.

Lekkas

Onof²,

Lee³

2013

Global NEST Journal

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In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when 'trained' using observed outputs. This paper utilises various types of ANNs in an attempt to assess the relative performance of existing models. Ali Efenti, a subcatchment of the river Pinios (Greece), is examined and the results support the hypothesis that ANNs can produce qualitative forecasts. A 7-hour ahead forecast in particular proves to be of fairly high precision, especially when an error prediction technique is introduced to the ANN models.

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“…SOM has found almost countless applications in fields such as pattern recognition, image analysis (Kohonen, 2001) and exploratory data analysis (Kaski, 1997). However, applications related to hydrological modelling still seem to be the exception (see Minns and Hall, 2005). It has been used by and Herbst and Casper (2008) for overall model evaluation and model identification purposes.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps

Herbst

Casper

Grundmann³

et al. 2009

Nat. Hazards Earth Syst. Sci.

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Abstract. Distributed watershed models constitute a key component in flood forecasting systems. It is widely recognized that models because of their structural differences have varying capabilities of capturing different aspects of the system behaviour equally well. Of course, this also applies to the reproduction of peak discharges by a simulation model which is of particular interest regarding the flood forecasting problem.In our study we use a Self-Organizing Map (SOM) in combination with index measures which are derived from the flow duration curve in order to examine the conditions under which three different distributed watershed models are capable of reproducing flood events present in the calibration data. These indices are specifically conceptualized to extract data on the peak discharge characteristics of model output time series which are obtained from Monte-Carlo simulations with the distributed watershed models NASIM, LAR-SIM and WaSIM-ETH. The SOM helps to analyze this data by producing a discretized mapping of their distribution in the index space onto a two dimensional plane such that their pattern and consequently the patterns of model behaviour can be conveyed in a comprehensive manner. It is demonstrated how the SOM provides useful information about details of model behaviour and also helps identifying the model parameters that are relevant for the reproduction of peak discharges and thus for flood prediction problems. It is further shown how the SOM can be used to identify those parameter sets from among the Monte-Carlo data that most closely approximate the peak discharges of a measured time series. The results represent the characteristics of the observed time series with partially superior accuracy than the reference simulaCorrespondence to: M. Herbst (herbstm@uni-trier.de) tion obtained by implementing a simple calibration strategy using the global optimization algorithm SCE-UA. The most prominent advantage of using SOM in the context of model analysis is that it allows to comparatively evaluating the data from two or more models. Our results highlight the individuality of the model realizations in terms of the index measures and shed a critical light on the use and implementation of simple and yet too rigorous calibration strategies.

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Artificial Neural Network Concepts in Hydrology

Cited by 8 publications

References 88 publications

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

Application of artificial neural networks for flood forecasting.

Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps

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