A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui, Dieu Tien; Hoang, Nhat‐Duc

doi:10.5194/gmd-10-3391-2017

Cited by 65 publications

(24 citation statements)

References 70 publications

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“…In this study, flood risk is assessed with the integration of various indicators of flood depth, population density, land use category, distance to rivers, poverty rate, and road density using AHP and spatial analysis techniques. Some previous studies in the field of flood risk analysis in Vietnam focused on the flood hazard assessments (for example, [17,39,78,79]). The present study used AHP method and spatial techniques to combine flood inundation map with flood exposure and vulnerability data to provide an integrated flood risk assessment map.…”

Section: Discussionmentioning

confidence: 99%

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Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam

et al. 2020

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Vietnam has been extensively affected by floods, suffering heavy losses in human life and property. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high-resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64,348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of Quang Binh province is a valuable tool and source for flood preparedness activities at the local scale.

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Section: Discussionmentioning

confidence: 99%

“…In this context, there have been many studies on flood risk analysis in Vietnam with a variety of approaches [17][18][19][20]. However, there is still a lack of studies on holistic flood risk assessments at local scales.…”

Section: Introductionmentioning

confidence: 99%

Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam

et al. 2020

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“…In the modelling, a knowledge of historical flash floods is important [24,48]. Thus, a flash flooding inventory map is essential.…”

Section: Flash Flood Inventorymentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

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106

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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“…Generation of the landslide maps using machine learning methods requires a knowledge of the historical landslide events [74,77]. Thus, the landslide inventory map is vital for determining the spatial links of historic earth slips with factors of geomorphological, hydrological, climatic factors, and anthropogenic activities [78,79].…”

Section: Landslide Inventorymentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

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A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Cited by 65 publications

References 70 publications

Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam

Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

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