Real-time flood forecasting with the use of inadequate data

Nalbantis, Ioannis

doi:10.1080/02626660009492324

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…It was found that using k = 4 and a four-step approximation for the trend, an optimal compromise could be found between accurate description of soil moisture variation and number of inputs. This procedure is in line with previous analysis by Nalbantis (2000), who found that the importance of rainfall resolution decreases moving back in time.…”

Section: Data Analysis and Model Set-upsupporting

confidence: 78%

Artificial neural network approach to flood forecasting in the River Arno

Campolo

Soldati²,

Andreussi

2003

Hydrological Sciences Journal

186

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The basin of the River Arno is a flood-prone area where flooding events have caused damage valued at more than 100 billion euro in the last 40 years. At present, the occurrence of an event similar to the 1966 flood of Firenze (Florence) would result in damage costing over 15.5 billion euro. Therefore, the use of flood forecasting and early warning systems is mandatory to reduce the economic losses and the risk for people. In this work, a flood forecasting model is presented that exploits the real-time information available for the basin (rainfall data, hydrometric data and information on dam operation) to predict the water-level evolution. The model is based on artificial neural networks, which were successfully used in previous works to predict floods in an unregulated basin and to predict water-level evolution in the Arno basin under low flow conditions. Accurate predictions are obtained using a two-year data set and a special treatment of input data; which allows a balance to be found between the spatial and temporal resolution of rainfall information and the model complexity. The prediction of water-level evolution remains accurate within a forecast time ahead of 6 h, which is the minimum time lag for the river to respond to dam releases under saturated conditions of the basin. The predicted flow rate percentage error ranges from 7 to 15% from the 1-h ahead to 6-h ahead predictions, and the accuracy of prediction increases for each time ahead of prediction, as the flow rate increases, suggesting that the model is particularly suited for flood forecasting purposes.Key words flood forecasting; artificial neural network; system response identification; nonlinear modelling; rainfall-runoff; River Arno, Italy Une approche à base de réseau de neurones artificiels pour la prévision des crues du fleuve Arno Résumé Le bassin du fleuve Arno est une zone sujette au phénomène des inondations, où le coût des dégâts dus aux inondations durant les 40 dernières années se chiffre à plus de 100 milliards d'euros. De nos jours, un événement aussi grave que l'inondation de 1966 à Florence produirait des dommages pour plus de 15.5 milliards d'euros. C'est pourquoi l'utilisation de systèmes de prévision et d'annonce précoce de crues s'avère nécessaire en vue d'une réduction des pertes économiques et du risque pour les personnes. Nous présentons dans ce travail un modèle de prévision de crues exploitant les informations en temps réel disponibles pour le bassin (données de précipitations, enregistrements hydrométriques et opérations de barrage), dans le but de prévoir l'évolution du niveau de l'eau. Le modèle est basé sur des réseaux de neurones artificiels qui ont été employés avec succès dans des travaux développés précédemment pour prévoir les crues dans un bassin non-régulé et pour prévoir Marina Campolo et al. 382l'évolution du niveau de l'eau dans le bassin de l'Arno en conditions d'écoulement réduit. Des prévisions précises sont obtenues à partir d'un jeu de données de deux ans et d'un traitement spécial des données ...

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Section: Data Analysis and Model Set-upsupporting

confidence: 78%

Artificial neural network approach to flood forecasting in the River Arno

Campolo

Soldati²,

Andreussi

2003

Hydrological Sciences Journal

186

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“…This method estimates state variables depending on the very last observed discharges. Consequently, the question of initial conditions appears to be less important if some (or even all) states are re-estimated by this updating technique (Nalbantis, 2000;Aubert et al, 2003;Moore et al, 2005).…”

Section: The Real-time Forecasting Specificitiesmentioning

confidence: 99%

“…This is a frequent situation when looking for high time resolution series. To bypass this obstacle, Nalbantis (1995) suggests relying on coarser data series (e.g. daily) to estimate fine (hourly) initial conditions.…”

Section: Continuous Vs Event-based Approaches To Modellingmentioning

confidence: 99%

How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

Berthet

Andréassian²,

Perrin³

et al. 2009

Hydrol. Earth Syst. Sci.

173

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Abstract. This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall-runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

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“…Lekkas et al 2001;Nalbantis 2000;Broersen 2007). The error correction method based on the time series model takes advantage of a tendency in the error sign to persist the sequences of positive (overestimation) or negative errors (underestimation), and it has proven to be more effective and simpler than the Kalman filter updating technique (Nalbantis 2000). Generally speaking, in using the time series models, including the autoregressive (AR) and moving average (MA) models, as well as the associated composite form (ARMA), the suitable order should be identified, which is commonly carried out using Akaike's AIC criterion, in advance.…”

Section: Introductionmentioning

confidence: 97%

“…Hence, the forecast error correction can be carried out by a time series model, such as a autoregressive and moving average (ARMA), which takes advantage of the autocorrelation of the past error (e.g. Lekkas et al 2001;Nalbantis 2000;Broersen 2007). The error correction method based on the time series model takes advantage of a tendency in the error sign to persist the sequences of positive (overestimation) or negative errors (underestimation), and it has proven to be more effective and simpler than the Kalman filter updating technique (Nalbantis 2000).…”

Section: Introductionmentioning

confidence: 99%

Real-time correction of water stage forecast during rainstorm events using combination of forecast errors

Lien

Chang

et al. 2011

Stoch Environ Res Risk Assess

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This study proposes a real-time error correction method for the forecasted water stage using a combination of forecast errors estimated by the time series models, AR(1), AR(2), MA(1) and MA(2), and the average deviation model to update the water stage forecast during rainstorm events. During flood forecasting and warning operations, the proposed real-time error correction method takes advantage of being individually and continuously implemented and the results not being updated to the hydrological model and hydraulic routings so as to save computational time by recalibrating the parameters of the proposed methods with real-time observation. For model validation, the current study adopts the observed and forecasted data on a severe typhoon, Morakot, collected at eight water level gauges in Southern Taiwan and provided by the flood forecast system FEWS_Taiwan, which is linked with the reliable quantitative precipitation forecast (QPF) at 3 h of lead time provided by the Center Weather Bureau in Taiwan, as the model validation. The results of numerical experiments indicate that the proposed real-time error correction method can effectively reduce the errors of forecasted water stages at the 1-, 2-, and 3-h lead time and so enhance the reliability of forecast information issued by the FEWS_Taiwan. By means of real-time estimating potential forecast error, the uncertainties in hydrology, modules as well as associated parameters, and physiographical features of the river can be reduced.

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Real-time flood forecasting with the use of inadequate data

Cited by 11 publications

References 19 publications

Artificial neural network approach to flood forecasting in the River Arno

Artificial neural network approach to flood forecasting in the River Arno

How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

Real-time correction of water stage forecast during rainstorm events using combination of forecast errors

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