Clustering-based hybrid inundation model for forecasting flood inundation depths

Estimation of flood depths and extents may provide disaster information for dealing with contingency and alleviating risk and loss of life and property. We present a two-stage procedure underlying CHIM (clustering-based hybrid inundation model), which is composed of linear regression models and ANNs (artificial neural networks) to build the regional flood inundation forecasting model. The two-stage procedure mainly includes data preprocessing and model building stages. In the data preprocessing stage, K-means clustering is used to categorize the data points of the different flooding characteristics in the study area and to identify the control point(s) from individual flooding cluster(s). In the model building stage, three classes of flood depth forecasting models are built in each cluster: the back-propagation neural network (BPNN) for each control point, the linear regression models for the grids that have highly linear correlation with the control point, and a multi-grid BPNN for the grids that do not have highly linear correlation with the control point. The practicability and effectiveness of the proposed approach is tested in the Dacun Township, Changhua County in Central Taiwan. The results show that the proposed CHIM can continuously and adequately provide 1-h-ahead flood inundation maps that well match the simulation flood inundation results and very effectively reduce 99% CPU time.

[1]  Gwo-Fong Lin,et al.  A SOM-based Approach to Estimating Design Hyetographs of Ungauged Sites , 2007 .

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[4]  Chris Kilsby,et al.  A weather-type approach to analysing water resource drought in the Yorkshire region from 1881 to 1998 , 2002 .

[5]  Fi-John Chang,et al.  Intelligent reservoir operation system based on evolving artificial neural networks , 2008 .

[6]  Oleg P. Arkhipkin,et al.  Space monitoring of floods in Kazakhstan , 2004, Math. Comput. Simul..

[7]  P. Bates,et al.  Predicting floodplain inundation: raster‐based modelling versus the finite‐element approach , 2001 .

[8]  Gwo-Fong Lin,et al.  A non-linear rainfall-runoff model using radial basis function network , 2004 .

[9]  Sang-Hyeok Kang,et al.  The application of integrated urban inundation model in Republic of Korea , 2009 .

[10]  R. Abrahart,et al.  Flood estimation at ungauged sites using artificial neural networks , 2006 .

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

[12]  François Anctil,et al.  Evaluation of Neural Network Streamflow Forecasting on 47 Watersheds , 2005 .

[13]  Sevket Durucan,et al.  River flow prediction using artificial neural networks: generalisation beyond the calibration range. , 2000 .

[14]  Yi Pan,et al.  Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property , 2005, IEEE Transactions on NanoBioscience.

[15]  Dynamic inundation simulation of storm water interaction between sewer system and overland flows , 2002 .

[16]  Ru-Yih Wang,et al.  Analyzing Hazard Potential of Typhoon Damage by Applying Grey Analytic Hierarchy Process , 2004 .

[17]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[18]  Hendrik Zwenzner,et al.  Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data , 2008 .

[19]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[20]  M. Horritt.  Calibration of a two‐dimensional finite element flood flow model using satellite radar imagery , 2000 .

[21]  Paul D. Bates,et al.  Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[23]  Matthew S. Horritt,et al.  A methodology for the validation of uncertain flood inundation models , 2006 .

[24]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[25]  P. Bates,et al.  Evaluation of 1D and 2D numerical models for predicting river flood inundation , 2002 .

[26]  Keith Beven,et al.  Bayesian updating of flood inundation likelihoods conditioned on flood extent data , 2004 .

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

[28]  Jörg Rahnenführer,et al.  Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering , 2002, Bioinform..

[29]  U. C. Kothyari,et al.  Modeling of the daily rainfall-runoff relationship with artificial neural network , 2004 .

[30]  Florian Pappenberger,et al.  High-Resolution 3-D Flood Information From Radar Imagery for Flood Hazard Management , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[31]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[32]  F. Chang,et al.  Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks , 2009 .