An Effective Two-Stage Neural Network Model and Its Application on Flood Loss Prediction

Li, Yang; Zuo, Chun; Wang, Yuguo

doi:10.1007/11427469_160

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Other advocated factors by Yang et al . () cited by Chang et al . () are meteorological (rainfall terrain) and flood prevention measures.…”

Section: Introductionmentioning

confidence: 96%

“…Besides the water depth factor, other flood characteristics such as velocity, sediments load, warning time and awareness, winds and duration are also incorporated in damage assessment analysis (Herath et al, 1999;Dutta and Herath, 2001;Herath and Wang, 2009). Other advocated factors by Yang et al (2005) cited by Chang et al (2008) are meteorological (rainfall terrain) and flood prevention measures.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of flood damage estimation to spatial resolution

Komolafe

Herath

Avtar

2015

J Flood Risk Management

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Flood damage estimation is an important element in flood risk assessment; it is useful for mobilizing investment and developing policies for flood loss prevention since most flood risk reduction measures are based on cost benefit analysis. From a global or regional view‐point, it is necessary to understand relative impacts on economies of countries to mobilize international support and develop response strategies. This requires a unified approach to flood damage estimation across countries and over large spatial extents. Spatial resolutions used in the assessment have a direct impact on the estimation of potential flood heights as well as representation of property exposure and, consequently, the flood damage estimates. Using Ichinomiya river basin, Japan, as a case study, the sensitivity of spatial scale in estimating potential flood economic damages is investigated and approaches are delineated to reduce errors arising from coarse spatial resolutions. [Correction added on 5 May 2016, after first online publication: in the first sentence of the abstract the word “is” has been inserted between “it” and “useful”.]

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“…Other advocated factors by Yang et al . () cited by Chang et al . () are meteorological (rainfall terrain) and flood prevention measures.…”

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Sensitivity of flood damage estimation to spatial resolution

Komolafe

Herath

Avtar

2015

J Flood Risk Management

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“…McBean et al (1988) pointed out that there were many factors besides flood depth that could affect the flood damage, such as time of year of flooding, velocity and sediment load of floodwaters, duration of flooding as well as the warning time, and therefore, it is recommended that the flood-damage function should be adjusted. Yang et al (2005) also noted that some meteorological, physiographic and human factors such as rainfall, terrain and flood-prevention measures could influence the actual flood damages. Hence, the relationships between various factors and flood damages are now widely examined.…”

Section: Introductionmentioning

confidence: 99%

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

Chang¹,

Lin²,

Su³

2019

WSA

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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.

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Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions

Zhang

et al. 2022

Environ Sci Pollut Res

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An Effective Two-Stage Neural Network Model and Its Application on Flood Loss Prediction

Cited by 4 publications

References 2 publications

Sensitivity of flood damage estimation to spatial resolution

Sensitivity of flood damage estimation to spatial resolution

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

Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions

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