Performance of conceptual and black-box models in flood warning systems

Abstract Flood forecasting is a core of flood forecasting and flood warning system which can be implemented by both conceptual rainfall–runoff (CRR) model and black-box rainfall–runoff (BBRR) model. Dynamic artificial neural network (DANN) as an innovative BBRR model and HEC-HMS as a traditional CRR model were used for flood forecasting. The aim of this paper is to compare the efficiency of HEC-HMS and DANN for the determination of flood warning lead-time (FWLT) in a steep urbanized watershed. A framework is proposed to compare the performance of the models based on four criteria: type and quantity of required input data by each model, flood simulation performance, FWLT and expected lead-time (ELT). Finally, the results show that FWLT and ELT were estimated longer by DANN than by HEC-HMS model. In brief, because of less required data by BBRR model and its longer ELT, future research should be focused on better verification of it.

[1]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[2]  Harry W. Dotson,et al.  Hydrologic Aspects of Flood Warning-Preparedness Programs , 1990 .

[3]  Nitin K. Tripathi,et al.  An artificial neural network model for rainfall forecasting in Bangkok, Thailand , 2008 .

[4]  Paulin Coulibaly,et al.  Comparison of neural network methods for infilling missing daily weather records , 2007 .

[5]  Slobodan P. Simonovic,et al.  Neural network approach to output updating for the physically-based model of the Upper Thames River watershed , 2012 .

[6]  L. A. Du Plessis,et al.  A review of effective flood forecasting, warning and response system for application in South Africa , 2002 .

[7]  Lynn E. Johnson.  Assessment of flash flood warning procedures. , 2000 .

[8]  Baxter E. Vieux,et al.  Ordered Physics‐Based Parameter Adjustment of a Distributed Model , 2013 .

[9]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[10]  Hubert Cardot,et al.  Study of the Behavior of a New Boosting Algorithm for Recurrent Neural Networks , 2005, ICANN.

[11]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[12]  Günter Blöschl,et al.  A compilation of data on European flash floods , 2009 .

[13]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[14]  Mohammad Ebrahim Banihabib,et al.  A dynamic artificial neural network for assessment of land-use change impact on warning lead-time of flood , 2015 .

[15]  Woonsup Choi.  Climate change, urbanisation and hydrological impacts , 2004 .

[16]  Peng Hong,et al.  Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network , 2008 .

[17]  Nathan Pingel,et al.  Estimating Forecast Lead Time , 2005 .

[18]  Maha Tawfik.  Linearity versus non-linearity in forecasting Nile river flows , 2003 .

[19]  Hyun Il Choi,et al.  Estimation of the Relative Severity of Floods in Small Ungauged Catchments for Preliminary Observations on Flash Flood Preparedness: A Case Study in Korea , 2012, International journal of environmental research and public health.

[20]  S. Sorooshian,et al.  Calibration of watershed models , 2003 .

[21]  Li-Chiu Chang,et al.  Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Ismail Abustan,et al.  Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modelling (a case study of Sungai Kayu Ara River basin, Malaysia) , 2012 .

[23]  Guangtao Fu,et al.  Classified real-time flood forecasting by coupling fuzzy clustering and neural network , 2010 .

[24]  Li-Chiu Chang,et al.  Real‐time recurrent learning neural network for stream‐flow forecasting , 2002 .

[25]  Xiang-Yang Li,et al.  A Web-based flood forecasting system for Shuangpai region , 2006, Adv. Eng. Softw..

[26]  Paulin Coulibaly,et al.  Nonstationary hydrological time series forecasting using nonlinear dynamic methods , 2005 .

[27]  Gürsel Serpen,et al.  Simultaneous recurrent neural network trained with non-recurrent backpropagation algorithm for static optimisation , 2003, Neural Computing & Applications.

[28]  N. Chan,et al.  The Malaysian flood hazard management program , 2003 .

[29]  Tienfuan Kerh,et al.  Neural networks forecasting of flood discharge at an unmeasured station using river upstream information , 2006, Adv. Eng. Softw..

[30]  Eric Huang,et al.  Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control , 2014 .

[31]  B. Saghafian,et al.  Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting , 2010 .

[32]  P. G. Samuels.  RIBAMOD: River basin modelling, management and flood mitigation , 1999 .

[33]  Yen-Chang Chen,et al.  Flood forecasting using radial basis function neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[34]  Jutta Thielen,et al.  The influence of historic land use changes and future planned land use scenarios on floods in the Oder catchment , 2003 .

[35]  M. Sugawara,et al.  On the analysis of runoff structure about several Japanese rivers. , 1961 .

[36]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[37]  L. See,et al.  An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment , 2002 .

[38]  Getnet Y. Muluye.  Improving long-range hydrological forecasts with extended Kalman filters , 2011 .

[39]  Yen-Ming Chiang,et al.  Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling , 2004 .

[40]  I. Andjelkovic,et al.  GUIDELINES ON NON-STRUCTURAL MEASURES IN URBAN FLOOD MANAGEMENT , 2001 .

[41]  P. Ayral,et al.  Using GIS for emergency management: a case study during the 2002 and 2003 flooding in south-east France , 2007 .

[42]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[43]  David M. Goldman,et al.  The New HEC-1 Flood Hydrograph Package. , 1981 .

[44]  Id Cluckie.  Ribamod: River Basin Modelling, Management and Flood Mitigation Concerted Action , 1998 .

[45]  Armando Brath,et al.  Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models , 2002 .