Urban flood hazard mapping using a hydraulic–GIS combined model

Feng, Boyu; Wang, Jinfei; Zhang, Ying; Hall, Barbara; Zeng, Chao

doi:10.1007/s11069-019-03850-7

Cited by 24 publications

(11 citation statements)

References 25 publications

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“…In order to the proposal of [30], the suitable structure of the alternative linear regression is a regression model through the origin (RTO), with the gradient as the only acceptable model-fitting parameter. The data were assessed with IBM SPSS again under both runoff coefficient conditions and identified the best-fitted RTO given by Equations ( 10) and (11), and their statistics shown in Tables 5 and 6.…”

Section: Modelmentioning

confidence: 99%

“…For Q/P > 50%, Q = 0.649P (10) For Q/P > 60%, Q = 0.724P (11) Table 5. Descriptive statistics of Equations ( 10) and ( 11) at α = 0.05.…”

Section: Modelmentioning

confidence: 99%

“…However, some reported that the usage of the CN in representing a watershed is arbitrary, vague, and often contradictory in describing related areas of land cover [2,9]. Other researchers emphasised the importance to apply a multi-modelling approach and statistical methods with land-use and cover variations in order to achieve better flood modelling results [10,11], while [12] even concluded that even very-fine-resolution topography and high-resolution land cover data may not be able to produce reliable urban flood modelling results using the HEC-RAS software. Some researchers also reported difficulty to determine an optimum λ value [13] in the SCS CN model.…”

Section: Introductionmentioning

confidence: 99%

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Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

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The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

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Section: Modelmentioning

confidence: 99%

“…For Q/P > 50%, Q = 0.649P (10) For Q/P > 60%, Q = 0.724P (11) Table 5. Descriptive statistics of Equations ( 10) and ( 11) at α = 0.05.…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

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“…Flood risk is commonly defined as the integration of flood hazard and flood vulnerability (UN/ ISDR, 2004;Wisner et al, 2004). Flood hazard is a scale of flooding, such as water depths and areas, which can be generated using hydraulic models under various rainfall intensities and discharges (Erena et al, 2018;Feng et al, 2020). Flood vulnerability reflects the susceptibility of people to a disaster.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps

Jang

Vohnicky

Kuo

2021

Water Resour Manage

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Flood risk maps are important references for disaster prevention and emergency response. The accuracy of risk maps is greatly affected by the resolutions of hazard and vulnerability maps. To determine the impact of map resolutions on flood risk analysis, a total of 12 risk maps were generated for Shanhua District, Taiwan, via the integration of hazard and vulnerability maps under two resolutions and three return periods. The hazard, vulnerability, and risk maps were classified into five levels according to flood depth, socio-economic indicators, and their products, respectively. The results show that the downscaling of hazard maps greatly increases the hit rate by 28% and decreases the false alarm rate by 53% in the flood risk analyses of households. In contrast, the downscaling of vulnerability maps only slightly increases the hit rate without an obvious decrease in the false alarm rate. To improve flood risk analysis under time and budget limitations, numerical downscaling of hazard maps should be given higher priority because it reduces the structural errors in hydraulic simulations that cannot be compensated for by the statistical downscaling of vulnerability maps.

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“…With satellite images delivering accurate data at high temporal resolution and with such high spatial precision that the need for conducting field surveys has been significantly obliterated. This has resulted in a plethora of mapping studies such as landslide susceptibility mapping (Bragagnolo et al 2020a, b;Hu et al 2020;Sameen et al 2020;Sansare and Mhaske 2020;Tang et al 2020;Van Dao et al 2020;Wang et al 2020;Wu et al 2020), flood susceptibility mapping (Bui et al 2020;Chen et al 2020a;Costache and Bui 2020;Feloni et al 2020;Feng et al 2020;Mishra and Sinha 2020;Pourghasemi et al 2020;Sansare and Mhaske 2020;Sarkar and Mondal 2020), and forest fire susceptibility mapping (Abedi Gheshlaghi et al 2020;Ç olak and Sunar 2020;Rahimi et al 2020;Sevinc et al 2020;Venkatesh et al 2020), mineral potential mapping (de Quadros et al 2006) etc., employing RS for obtaining data for regions that were traditionally considered inaccessible. These studies have focused on generating zonation maps delineating the zones on the basis of their relative potential/susceptibility/vulnerability/proneness using a variety of statistical techniques such as weight-of-evidence (Mastere 2020;Zaheer et al 2020;Rahmati et al 2016;Kayastha et al 2012;Ozdemir 2011;Corsini et al 2009;de Quadros et al 2006;Lee and Choi 2004), frequency ratio (Sarkar and Mondal 2020;Rahmati et al 2016;Naghibi et al 2015;…”

Section: Introductionmentioning

confidence: 99%

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

Sachdeva

Kumar

2020

Stoch Environ Res Risk Assess

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In the drought prone district of Dholpur in Rajasthan, India, groundwater is a lifeline for its inhabitants. With population explosion and rapid urbanization, the groundwater is being critically over-exploited. Hence the current groundwater potential mapping study was undertaken to ascertain the areas that are more likely to yield a larger volume of groundwater against those areas that have poor groundwater potential and accordingly perpetuate the much needed damage control. Thematic layers for 14 groundwater influencing factors were considered for the study region, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), geology, soil, land use, normalized difference vegetation index (NDVI), surface temperature, precipitation, distance from roads, and distance from rivers. These were then subjected to an overlay operation, with the groundwater inventory which comprised of the locations of observational groundwater wells. The resulting geospatial database was then used to train two decision tree based ensemble models: gradient boosted decision trees (GBDT) and random forest (RF). The predictive performance of these models was then compared using various performance metrics such as area under curve (AUC) of receiver operating characteristics (ROC), sensitivity, accuracy, etc. It was found that GBDT (AUC: 0.79) outperformed RF (AUC: 0.71). The validated GBDT model was then used to construct the groundwater potential zonation map. The generated map showed that about 20.2% of the region has very high potential, while 22.6% has high potential to yield groundwater, and approximately 19.9-17.5% of the study region has very low to low groundwater potential.

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Urban flood hazard mapping using a hydraulic–GIS combined model

Cited by 24 publications

References 25 publications

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Improvement of Flood Risk Analysis Via Downscaling of Hazard and Vulnerability Maps

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

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