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Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738

Water SA vol.34 n.2 Pretoria Feb. 2008

 

Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation

 

 

Ling-Fang ChangI; Chun-Hung LinI; Ming-Daw SuII

IDepartment of Bioenvironmental Systems Engineering, National Taiwan University
IIDepartment of Bioenvironmental Systems Engineering, National Taiwan University, No.1, sec. 4, Roosevelt Rd. Taipei, Taiwan 10617

Correspondence

 

 


ABSTRACT

Flood damage functions are necessary to ensure comprehensive flood-risk management. This study attempts to establish a residential flood-damage function through interviewing the residents living in the region where flood disasters occur frequently. Keelung River basin, near Taipei Metropolitan in Taiwan was selected as study area. Flood damages are related to the flood depths, which are the most commonly considered factor in previously published work. Ordinary least squares (OLS) regression was used to construct the flood-damage function at the beginning. Analytical results indicate that flood depth is the significant variable, but the spatial pattern of the residuals shows that residuals exhibit spatial autocorrelation. The Geographically Weighted Regression (GWR) Model was then applied to modify the traditional regression model, which cannot capture spatial variations, and to reduce the problem of spatial autocorrelation. The R-square value was found to increase from 0.15 to 0.24, and the spatial autocorrelation in the residuals was no longer evident. A modified OLS model with a dummy variable to capture the spatial autocorrelation pattern was also proposed for future applications. In conclusion, the residential flood damage is determined by flood depth and zone, and the GWR model not only captures the spatial variations of the affecting factors, but also helps to discover the independent variable to modify the traditional regression model.

Keywords: flood damage, flood depth, OLS, GWR, spatial autocorrelation


 

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Reference

BAILEY TC and GATRELL AC (1995) Interactive Spatial Data Analysis. Wiley, New York.         [ Links ]

BARO-SUAREZ JE, DIAZ-DELGADO C, ESTELLER-ALBERICH MV and CALDERON G (2007) Economic flood loss estimation curves for Mexican rural and residential areas. Part 1: Methodology proposal. Ingenieria Hidraulica En Mexico 32 (1) 91-102.         [ Links ]

BECK J, METZGER R, HINGRAY B and MUST A (2002) Flood risk assessment based on security deficit analysis. Paper presented at the 27th General Assembly of the European Geophysical Society Geophys. Res. 21- 26 April 2002, Nice, France.         [ Links ]

BLACK RD (1975) Flood Proofing Rural Structures: A 'Project Agnes' Report, Pennsylvania. Final report prepared for the United States Department of Commerce, Economic Development Administration. National Technical Information Service, Springfield, VA, USA, May 1975.         [ Links ]

BRUNSDON C, FOTHERINGHAM AS and CHARLTON ME (1996) Geographically weighted regression: a method for exploring spatial non-stationarity. Geogr. Anal. 28 (4) 281-298.         [ Links ]

CHANG LF (2000) Flood Damage Estimation for Residential Area. M.Sc Thesis, National Taiwan University (in Chinese).         [ Links ]

CHANG LF and SU MD (2001) Application of spatial data to damage estimations in flood. J. Chin. Agric. Eng. 47 (1) 20-28 (in Chinese).         [ Links ]

CH2M HILL (1974) Potential Flood Damages. Willamette River System, Department of the Army Portland District, Corps of Engineers, Portland, OR, USA.         [ Links ]

DAVID TF (2001) Flood-warning decision-support system for Sacramento, California. Water Resour. Plann. Manage. 127 (4) 254-260.         [ Links ]

DU PLESSIS LA (2002) A review of effective flood forecasting, warning and response system for application in South Africa. Water SA 28 (2) 129-137. http://www.wrc.org.za/archives/watersa%20archive/2002/April/1375.pdf        [ Links ]

FEMA (1977) Reducing Flood Damage through Building Design: A Guide Manual - Elevated Residential Structures. FEM Agency (ed.         [ Links ])

FIA (1970) Flood Hazard Factors, Depth-Damage Curves, Elevation Frequency Curves, Standard Rate Tables. US Federal Insurance Administration.         [ Links ]

FOTHERINGHAM AS, BRUNSDON C and CHARLTON ME (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester.         [ Links ]

FOTHERINGHAM AS, BRUNSDON C and CHARLTON ME (2000) Quantitative Geography. Sage, London.         [ Links ]

FOTHERINGHAM AS, BRUNSDON C and CHARLTON ME (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ. Plann. a 30 (11) 1905-1927.         [ Links ]

GRIGG NS and HEIWEG OJ (1974) Estimating Direct Residential Flood Damage in Urban Areas. Colorado State University, Colorado, USA.         [ Links ]

GRIGG NS (1985) Water Resources Planning. McGraw-Hill, New York.         [ Links ]

GRIGG NS and HEIWEG OJ (1975) State-of-the-art of estimating flood damage in urban areas. Water Resour. Bull. 11 (2) 379-390.         [ Links ]

GRIGG NS (1996) Water Resources Management. McGraw-Hill, New York.         [ Links ]

HUBERT G, DEUTSCH JC and DESBORDES MC (1996) Policy decision support systems: Modeling of rainfall flood damages. In: Penning-Rowsell E (ed.) Improving Flood Hazard Management across Europe. European Union Environment Program, Contract Number EV5V-CT93-0296, EURO Flood II, Chapter 3.         [ Links ]

KANG JL, SU MD and CHANG LF (2005) Loss functions and framework for regional flood damage estimation in residential area. J. Mar. Sci. Technol. 13 (3) 193-199.         [ Links ]

KRASOVSKAIA I (2001) Perception of the risk of flooding: The case of the 1995 flood in Norway. Hydrol. Sci. J. - Journal Des Sciences Hydrologiques 46 (6) 855-868.         [ Links ]

KUPFER JA and FARRIS CA (2007) Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models. Landscape Ecol. 22 (6) 837-852.         [ Links ]

LEKUTHAI A and VONGVISESSOMJAI S (2001) Intangible flood damage quantification. Water. Resour. Manage. 15 (5) 343-362.         [ Links ]

McBEAN EA, GORRIE J, FORTIN M, DING J, and MOULTON R (1988) Adjustment factors for flood damage curves. J. Water Resour. Plann. Manage. 114 (6) 635-646.         [ Links ]

McPHERSON HJ and SAARINEN TF (1977) Flood plain dwellers perception of flood hazard in Tucson. Arizona. Ann. Reg. Sci. 11 (2) 25-40.         [ Links ]

PLATT RV (2004) Global and local analysis of fragmentation in a mountain region of Colorado. Agric. Ecosyst. Environ. 101 (2-3) 207-218.         [ Links ]

PENNING-ROWSELL EC, TUNSTALL SM, TAPSELL SM and PARKER DJ (2000) The benefits of flood warnings: Real but elusive, and politically significant. J. Chart. Inst. Water Environ. Manage. 14 (1) 7-14.         [ Links ]

SHAW DG, HUANG HH and HO MC (2005) Modeling flood loss and risk perception: the case of typhoon Nari in Taipei. Proc. 5th Annu. IIASA-DPRI Meeting on Integrated Disaster Risk Management: Innovations in Science and Policy. 13-18 September, Beijing, China.         [ Links ]

SMITH DI (1994) Flood damage estimation - A review of urban stage-damage curves and loss function. Water SA 20 (3) 231-239.         [ Links ]

SU MD, KANG JL, CHANG LF and CHEN AS (2005) A grid-based GIS approach to regional flood damage assessment. J. Mar. Sci. Technol. 13 (3) 184-192.         [ Links ]

TAIWAN WATER RESOURCE AGENCY (1997) National Flood Insurance Program Pilot Study: A Case Study for Tang-Dee-Yang area. Taiwan project report, Taiwan (in Chinese).         [ Links ]

THIEKEN AH, MULLER M; KREIBICH H and MERZ B (2005) Flood damage and influencing factors: New insights from the August 2002 flood in Germany. Water. Resour. Res. 41 (12) W12430_ 1-16.         [ Links ]

TORTEROTOT JP, KAUARK-LEITE LA and ROCHE PA (1992) Analysis of individual real-time responses to flooding and influence on damage to households. Proc. Paper presented at the 3rd Int. Conf. on Floods and Flood Management. 24-26 November 1992, Florence, Italy.         [ Links ]

WIND HG, NIEROP TM, DE BLOIS CJ and DE KOK JL (1999) Analysis of flood damages from the 1993 and 1995 Meuse floods. Water Resour. Res. 35 (11) 3459-3465.         [ Links ]

YANG L, ZUO C and WANG YG (2005) An effective two-stage neural network model and its application on flood loss prediction. Proc. Advances in Neural Networks - Isnn 2005, Pt 3 Vol: 3498 1010-1016.         [ Links ]

ZHANG LJ, GOVE JH and HEATH LS (2005) Spatial residual analysis of six modelling techniques. Ecol. Model. 186 (2) 154-177.         [ Links ]

ZHANG LJ, BI HQ, CHENG PF and DAVIS CJ (2004) Modelling spatial variation in tree diameter-height relationships. Forest Ecol. Manage. 189 (1-3) 317-329.         [ Links ]

 

 

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E-mail: sumd@ntu.edu.tw

Received 23 July 2007
Accepted in revised form 15 December 2008