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Chinh Luu
Faculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi, Vietnam

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Original paper
Published: 25 June 2021 in Natural Hazards
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Flash flood is one of the most common natural hazards affecting many mountainous areas. Previous studies explored flash flood susceptibility models; however, there is still a lack of case studies in the transport sector. This paper aimed to develop advanced hybrid machine learning (ML) algorithms for flash flood susceptibility modeling and mapping using data from the road network National Highway 6 in Hoa Binh province, Vietnam. A single ML model of reduced error pruning trees (REPT) and four hybrid ML models of Decorate-REPT, AdaBoostM1-REPT, Bagging-REPT, and MultiBoostAB-REPT were applied to develop flash flood susceptibility maps. Field surveys were conducted about the flash flood locations on the 115-km route length of the National Highway 6 in 2017, 2018, and 2019 flood events. This study used 88 flash flood locations and 14 flood conditioning factors to construct and validate the proposed models. Statistical metrics, including sensitivity, specificity, accuracy, root mean square error, and area under the receiver operating characteristic curve, were applied to evaluate the models’ performance and accuracy. The DCREPT model showed the best performance (AUC = 0.988) among the training models and had the highest prediction accuracy (AUC = 0.991) among the testing models. We found that 12,572 ha (Decorate-REPT), 9564 ha (AdaBoostM1-REPT), 11,954 ha (Bagging-REPT), 14,432 ha (MultiBoostAB-REPT), and 17,660 ha (REPT) of the 3-km buffer area of the highway are in the high- and very high-flash-flood-susceptibility areas. The proposed methodology could be potentially generalized to other transportation routes in mountainous areas to generate flash flood susceptibility prediction maps.

ACS Style

Hang Ha; Chinh Luu; Quynh Duy Bui; Duy-Hoa Pham; Tung Hoang; Viet-Phuong Nguyen; Minh Tuan Vu; Binh Thai Pham. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural Hazards 2021, 1 -24.

AMA Style

Hang Ha, Chinh Luu, Quynh Duy Bui, Duy-Hoa Pham, Tung Hoang, Viet-Phuong Nguyen, Minh Tuan Vu, Binh Thai Pham. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Natural Hazards. 2021; ():1-24.

Chicago/Turabian Style

Hang Ha; Chinh Luu; Quynh Duy Bui; Duy-Hoa Pham; Tung Hoang; Viet-Phuong Nguyen; Minh Tuan Vu; Binh Thai Pham. 2021. "Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models." Natural Hazards , no. : 1-24.

Original paper
Published: 10 June 2021 in Natural Hazards
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Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In particular, Quang Binh province is often affected by floods and storms over the year. However, it still lacks studies on flood hazard estimation and prediction tools in this area. This study aims to develop a flooding susceptibility assessment tool using various machine learning (ML) techniques namely alternating decision tree (AD Tree), logistic model tree (LM Tree), reduced-error pruning tree (REP Tree), J48 decision tree (J48) and Naïve Bayes tree (NB Tree); historical flood marks; and available data of topography, hydrology, geology, and environment considering Quang Binh province as a study area. We used flood mark locations of major flooding events in the years 2007, 2010, and 2016; and ten flood conditioning factors to construct and validate the ML models. Various validation methods, including area under the ROC curve (AUC), were used to validate and compare the models. The result of the models’ validation suggests that all models have good performance: AD Tree (AUC = 0.968), LM Tree (AUC = 0.967), REP Tree (AUC = 0.897), J48 (AUC = 0.953), and NB Tree (AUC = 0.986). Out of these, NB Tree managed to achieve the best performance in terms of flood prediction with an accuracy higher than 92 %. The final flood susceptibility map highlights 6,265 km2 (78.8 % area) with a very low flooding hazard, 391 km2 (4.9 % area) with a low flooding hazard, 224 km2 (2.8 % area) with a moderate flooding hazard, 243 km2 (3.1 %) with a high flooding hazard, and 829 km2 (10.4 % area) with very high flooding hazard. The final flooding susceptibility assessment map could add a valuable source for flood risk reduction and management activities of Quang Binh province.

ACS Style

Chinh Luu; Quynh Duy Bui; Romulus Costache; Luan Thanh Nguyen; Thu Thuy Nguyen; Tran Van Phong; Hiep Van Le; Binh Thai Pham. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Natural Hazards 2021, 108, 3229 -3251.

AMA Style

Chinh Luu, Quynh Duy Bui, Romulus Costache, Luan Thanh Nguyen, Thu Thuy Nguyen, Tran Van Phong, Hiep Van Le, Binh Thai Pham. Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Natural Hazards. 2021; 108 (3):3229-3251.

Chicago/Turabian Style

Chinh Luu; Quynh Duy Bui; Romulus Costache; Luan Thanh Nguyen; Thu Thuy Nguyen; Tran Van Phong; Hiep Van Le; Binh Thai Pham. 2021. "Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam." Natural Hazards 108, no. 3: 3229-3251.

Journal article
Published: 29 May 2021 in Journal of Hydrology
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Recently, floods are occurring more frequently every year around the world due to increased anthropogenic activities and climate change. There is a need to develop accurate models for flood susceptibility prediction and mapping, which can be helpful in developing more efficient flood management plans. In this study, the Partial Decision Tree (PART) classifier and the AdaBoost, Bagging, Dagging, and Random Subspace ensembles learning techniques were combined to develop novel GIS-based ensemble computational models (ABPART, BPART, DPART and RSSPART) for flood susceptibility mapping in the Quang Binh Province, Vietnam. In total, 351 flood locations were used in the model study. This data was divided into a 70:30 ratio for model training (70% ≅ 255 locations) and (30% ≅ 96 locations) for model validation. Ten flood influencing factors, namely elevation, slope, curvature, flow direction, flow accumulation, river density, distance from river, rainfall, land-use, and geology, were used for the development of models. The OneR feature selection method was used to select and prioritize important factors for the spatial modeling. The results revealed that land-use, geology, and slope are the most important conditioning factors in the occurrence of floods in the study area. Standard statistical methods, including the ROC curve (AUC), were used for the performance evaluation of models. Results indicated that the performance of all models was good (AUC > 0.9) and RSSPART (AUC = 0.959) outperformed the others. Thus, the RSSPART model can be used for accurately predicting and mapping flood susceptibility.

ACS Style

Chinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology 2021, 599, 126500 .

AMA Style

Chinh Luu, Binh Thai Pham, Tran Van Phong, Romulus Costache, Huu Duy Nguyen, Mahdis Amiri, Quynh Duy Bui, Luan Thanh Nguyen, Hiep Van Le, Indra Prakash, Phan Trong Trinh. GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam. Journal of Hydrology. 2021; 599 ():126500.

Chicago/Turabian Style

Chinh Luu; Binh Thai Pham; Tran Van Phong; Romulus Costache; Huu Duy Nguyen; Mahdis Amiri; Quynh Duy Bui; Luan Thanh Nguyen; Hiep Van Le; Indra Prakash; Phan Trong Trinh. 2021. "GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam." Journal of Hydrology 599, no. : 126500.

Journal article
Published: 25 February 2021 in Knowledge-Based Systems
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In this paper, we proposed a novel approach for flood risk assessment, which is a combination of a deep learning algorithm and Multi-Criteria Decision Analysis (MCDA). The framework of the flood risk assessment involves three main elements: hazard, exposure, and vulnerability. For this purpose, one of the flood-prone areas of Vietnam, namely Quang Nam province was selected as the study area. Data of 847 past flood locations of this area was analyzed to generate training and testing datasets for the models. In this study, we have used one of the popular Deep Neural Networks (DNNs) algorithm for generation of flood susceptibility map while Analytic Hierarchy Process (AHP), which is a popular MCDA approach, was used to generate the hazard, exposure, and vulnerability maps. We have also used hybrid models namely BFPA and DFPA which are the ensembles of Bagging and Decorate with Forest by Penalizing Attributes algorithm for the comparison of performance with DNNs method. Various standard statistical indices including Receiver Operating Characteristic (ROC) curves were used for the performance evaluation and validation of the models. Results indicated that integration of DNNs and MCDA models is a promising approach for developing accurate flood risk assessment map of an area for the better flood hazard management.

ACS Style

Binh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems 2021, 219, 106899 .

AMA Style

Binh Thai Pham, Chinh Luu, Dong Van Dao, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Jason von Meding, Indra Prakash. Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems. 2021; 219 ():106899.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Dong Van Dao; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Jason von Meding; Indra Prakash. 2021. "Flood risk assessment using deep learning integrated with multi-criteria decision analysis." Knowledge-Based Systems 219, no. : 106899.

Journal article
Published: 30 November 2020 in Journal of Hydrology
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Flood risk assessment is an important task for disaster management activities in flood-prone areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study, we proposed a flood risk assessment framework which combines flood susceptibility assessment and flood consequences (human health and financial impact) for developing a final flood risk assessment map using Multi-Criteria Decision Analysis (MCDA) method. Two hybrid Artificial Intelligence (AI) models, namely ABMDT (AdaBoost-DT) and BDT (Bagging-DT) were developed with Decision Table (DT) as a base classifier for creating a flood susceptibility map. We used 847 flood locations of major flooding events in the years 2007, 2009 and 2013 in Quang Nam province of Vietnam; and 14 flood influencing factors of topography, geology, hydrology and environment to construct and validate the hybrid AI models. Various statistical measures were used to validate the models, including the Area Under Receiver Operating Characteristic (ROC) Curve called AUC. Results show that all the proposed models performed well, but the performance of the BDT model (AUC=0.96) is the best in comparison to other models ABMDT (AUC=0.953) and single DT (AUC=0.929). Therefore, the flood susceptibility map produced by the BDT model was used to combine with a flood consequences map to develop a reliable flood risk assessment map for the study area. The final flood risk map can provide a useful source for better flood hazard management of the study area, and the proposed framework and models can be applied to other flood-prone areas.

ACS Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology 2020, 592, 125815 .

AMA Style

Binh Thai Pham, Chinh Luu, Tran Van Phong, Huu Duy Nguyen, Hiep Van Le, Thai Quoc Tran, Huong Thu Ta, Indra Prakash. Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology. 2020; 592 ():125815.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Huu Duy Nguyen; Hiep Van Le; Thai Quoc Tran; Huong Thu Ta; Indra Prakash. 2020. "Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam." Journal of Hydrology 592, no. : 125815.

Journal article
Published: 13 October 2020 in Journal of Hydrology
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This paper introduces a new deep-learning algorithm of deep belief network (DBN) based on an extreme learning machine (ELM) that is structured by back propagation (BN) and optimized by particle swarm optimization (PSO) algorithm, named DEBP, for flood susceptibility mapping in the Vu Gia-Thu Bon watershed, central Vietnam. We use 847 locations of floods that occurred in 2007, 2009, and 2013 and 16 flood conditioning factors evaluated by an information gain ratio (IGR) technique to construct and validate the proposed model. Statistical metrics, including sensitivity, specificity, accuracy, F1-measure, Jaccard coefficient, Matthews correlation coefficient (MCC), root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), are used to assess the goodness-of-fit/performance and prediction accuracy of the new deep learning model. We further compare the proposed model with several well-known machine learning algorithms, including artificial neural network-based radial base function (ANNRBF), logistic regression (LR), logistic model tree (LMTree), functional tree (FTree), and alternating decision tree (ADTree). The new proposed model, DEBP, has the highest goodness-of-fit (AUC = 0.970) and prediction accuracy (AUC = 0.967) of all of the tested models and thus shows promise as a tool for flood susceptibility modeling. We conclude that novel deep learning algorithms such as the one used in this study can improve the accuracy of flood susceptibility maps that are required by planners, decision makers, and government agencies to manage of areas vulnerable to flood-induced damage.

ACS Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Phan Trong Trinh; Ataollah Shirzadi; Somayeh Renoud; Shahrokh Asadi; Hiep Van Le; Jason von Meding; John J. Clague. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology 2020, 592, 125615 .

AMA Style

Binh Thai Pham, Chinh Luu, Tran Van Phong, Phan Trong Trinh, Ataollah Shirzadi, Somayeh Renoud, Shahrokh Asadi, Hiep Van Le, Jason von Meding, John J. Clague. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology. 2020; 592 ():125615.

Chicago/Turabian Style

Binh Thai Pham; Chinh Luu; Tran Van Phong; Phan Trong Trinh; Ataollah Shirzadi; Somayeh Renoud; Shahrokh Asadi; Hiep Van Le; Jason von Meding; John J. Clague. 2020. "Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?" Journal of Hydrology 592, no. : 125615.

Journal article
Published: 29 May 2020 in Science of The Total Environment
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Water deficiency due to climate change and the world's population growth increases the demand for the water industry to carry out vulnerability assessments. Although many studies have been done on climate change vulnerability assessment, a specific framework with sufficient indicators for water vulnerability assessment is still lacking. This highlights the urgent need to devise an effective model framework in order to provide water managers and authorities with the level of water exposure, sensitivity, adaptive capacity and water vulnerability to formulate their responses in implementing water management strategies. The present study proposes a new approach for water quantity vulnerability assessment based on remote sensing satellite data and GIS ModelBuilder. The developed approach has three layers: (1) data acquisition mainly from remote sensing datasets and statistical sources; (2) calculation layer based on the integration of GIS-based model and the Intergovernmental Panel on Climate Change's vulnerability assessment framework; and (3) output layer including the indices of exposure, sensitivity, adaptive capacity and water vulnerability and spatial distribution of remote sensing indicators and these indices in provincial and regional scale. In total 27 indicators were incorporated for the case study in Vietnam based on their availability and reliability. Results show that the most water vulnerable is the South Central Coast of the country, followed by the Northwest area. The novel approach is based on reliable and updated spatial-temporal datasets (soil water stress, aridity index, water use efficiency, rain use efficiency and leaf area index), and the incorporation of the GIS-based model. This framework can then be applied effectively for water vulnerability assessment of other regions and countries.

ACS Style

Thu Thuy Nguyen; Huu Hao Ngo; Wenshan Guo; Hong Quan Nguyen; Chinh Luu; Kinh Bac Dang; Yiwen Liu; Xinbo Zhang. New approach of water quantity vulnerability assessment using satellite images and GIS-based model: An application to a case study in Vietnam. Science of The Total Environment 2020, 737, 139784 .

AMA Style

Thu Thuy Nguyen, Huu Hao Ngo, Wenshan Guo, Hong Quan Nguyen, Chinh Luu, Kinh Bac Dang, Yiwen Liu, Xinbo Zhang. New approach of water quantity vulnerability assessment using satellite images and GIS-based model: An application to a case study in Vietnam. Science of The Total Environment. 2020; 737 ():139784.

Chicago/Turabian Style

Thu Thuy Nguyen; Huu Hao Ngo; Wenshan Guo; Hong Quan Nguyen; Chinh Luu; Kinh Bac Dang; Yiwen Liu; Xinbo Zhang. 2020. "New approach of water quantity vulnerability assessment using satellite images and GIS-based model: An application to a case study in Vietnam." Science of The Total Environment 737, no. : 139784.

Journal article
Published: 16 April 2020 in International Journal of Environmental Research and Public Health
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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

ACS Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Sushant K. Singh; Nadhir Al-Ansari; John J. Clague; Abolfazl Jaafari; Wei Chen; Shaghayegh Miraki; Jie Dou; Chinh Luu; Krzysztof Górski; Binh Thai Pham; Huu Duy Nguyen; Baharin Bin Ahmad. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. International Journal of Environmental Research and Public Health 2020, 17, 2749 .

AMA Style

Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Sushant K. Singh, Nadhir Al-Ansari, John J. Clague, Abolfazl Jaafari, Wei Chen, Shaghayegh Miraki, Jie Dou, Chinh Luu, Krzysztof Górski, Binh Thai Pham, Huu Duy Nguyen, Baharin Bin Ahmad. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. International Journal of Environmental Research and Public Health. 2020; 17 (8):2749.

Chicago/Turabian Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Sushant K. Singh; Nadhir Al-Ansari; John J. Clague; Abolfazl Jaafari; Wei Chen; Shaghayegh Miraki; Jie Dou; Chinh Luu; Krzysztof Górski; Binh Thai Pham; Huu Duy Nguyen; Baharin Bin Ahmad. 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms." International Journal of Environmental Research and Public Health 17, no. 8: 2749.

Journal article
Published: 10 April 2020 in Sustainability
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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.

ACS Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability 2020, 12, 3058 .

AMA Style

Chinh Luu, Hieu Xuan Tran, Binh Thai Pham, Nadhir Al-Ansari, Thai Quoc Tran, Nga Quynh Duong, Nam Hai Dao, Lam Phuong Nguyen, Huu Duy Nguyen, Huong Thu Ta, Hiep Van Le, Jason Von Meding. Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam. Sustainability. 2020; 12 (7):3058.

Chicago/Turabian Style

Chinh Luu; Hieu Xuan Tran; Binh Thai Pham; Nadhir Al-Ansari; Thai Quoc Tran; Nga Quynh Duong; Nam Hai Dao; Lam Phuong Nguyen; Huu Duy Nguyen; Huong Thu Ta; Hiep Van Le; Jason Von Meding. 2020. "Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam." Sustainability 12, no. 7: 3058.

Journal article
Published: 16 April 2019 in International Journal of Disaster Risk Reduction
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Flood vulnerability is increasing in Vietnam due to rapid population growth, urbanization, and industrial development. Although there has been a significant increase in studies on flood risk assessments at the local scales in Vietnam, there remains a lack of tools developed for use at the national scale. In response to this need, we sourced and analyzed flood damage data from 1989 to 2015 in Vietnam's national disaster database in order to assess the flood risk of Vietnam's regions and provinces. The study proposes a new approach, multiple linear regression-TOPSIS, to analyze the disaster data. Our findings show that the North Central Coast is the most vulnerable region, followed by the Mekong Delta. Nghe An province has the highest flood risk score while Ho Chi Minh city has the lowest flood risk. This assessment tool provides accessible risk information for decision-makers and planners to classify the most at-risk areas and has practical implications for flood risk management at the national and local scales. The study also provides a new method for analyzing Vietnam's national disaster database, which may suggest potential applications for disaster loss databases in other countries and regions.

ACS Style

Chinh Luu; Jason von Meding; Mohammad Mojtahedi. Analyzing Vietnam's national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS. International Journal of Disaster Risk Reduction 2019, 40, 101153 .

AMA Style

Chinh Luu, Jason von Meding, Mohammad Mojtahedi. Analyzing Vietnam's national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS. International Journal of Disaster Risk Reduction. 2019; 40 ():101153.

Chicago/Turabian Style

Chinh Luu; Jason von Meding; Mohammad Mojtahedi. 2019. "Analyzing Vietnam's national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS." International Journal of Disaster Risk Reduction 40, no. : 101153.

Chapter
Published: 17 August 2018 in Urban Ecology
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Floods and storms have had a severe impact on the people of Vietnam over many years, particularly regarding an unacceptably high death toll. However, it still lacks studies on flood-related fatalities in Vietnam. This research aims to explore flood fatalities on a national scale and analyze damage-influencing attributes related to flood fatalities using the national disaster database of Vietnam and statistical learning approach. Records covering 27 years from 1989 to 2015 indicate at least 14,927 flood mortalities in Vietnam. The analysis results of statistical learning methods show that housing impact factor has the most considerable influence on flood fatalities. The results can provide implications for housing policies for the poor in flood-prone areas. The objective of reduction in mortality in disasters is under Goal 11 of Sustainable Development Goals.

ACS Style

Chinh Luu; Jason Von Meding. Analyzing Flood Fatalities in Vietnam Using Statistical Learning Approach and National Disaster Database. Urban Ecology 2018, 197 -205.

AMA Style

Chinh Luu, Jason Von Meding. Analyzing Flood Fatalities in Vietnam Using Statistical Learning Approach and National Disaster Database. Urban Ecology. 2018; ():197-205.

Chicago/Turabian Style

Chinh Luu; Jason Von Meding. 2018. "Analyzing Flood Fatalities in Vietnam Using Statistical Learning Approach and National Disaster Database." Urban Ecology , no. : 197-205.

Journal article
Published: 01 June 2018 in International Journal of Disaster Risk Reduction
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Vietnam has been extensively impacted by flooding over the years, sustaining heavy losses in human life and damages to housing, agriculture, and transportation. Flood risk in Vietnam is not widely understood beyond a very hazard-focused conceptualization, which often neglects to consider human vulnerability. The objective of this paper is to understand flood risk management (FRM) activities at local levels in Quang Nam province in Vietnam, along with legal and institutional frameworks that are intended to focus, but often restrict, policy and practice. Vietnam's legal and institutional frameworks are analyzed to provide an overview of the scope of existing FRM activities in Vietnam. We then examine the extent to which FRM in Vietnam follows recognized theoretical frameworks, and pinpoint where practice might be strengthened. Based on this positioning, we conduct 27 individual interviews with decision-makers in FRM at provincial, district, and commune levels in Quang Nam province. We argue that FRM activities at local levels in Vietnam are implemented according to the hierarchical structure of the political system and the responsibilities of various paramount government agencies, and that there is a lack of participation of experts, researchers, and scientists in steering committees. There is an urgent need for greater public participation in FRM at local levels. Since communes have a better understanding of their local conditions, empowering them with planning and decision-making power is necessary to improve the effectiveness of FRM activities. Our detailed analysis of FRM activities at local levels has implications for future efforts to mitigate flooding in Vietnam.

ACS Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. Flood risk management activities in Vietnam: A study of local practice in Quang Nam province. International Journal of Disaster Risk Reduction 2018, 28, 776 -787.

AMA Style

Chinh Luu, Jason Von Meding, Sittimont Kanjanabootra. Flood risk management activities in Vietnam: A study of local practice in Quang Nam province. International Journal of Disaster Risk Reduction. 2018; 28 ():776-787.

Chicago/Turabian Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. 2018. "Flood risk management activities in Vietnam: A study of local practice in Quang Nam province." International Journal of Disaster Risk Reduction 28, no. : 776-787.

Journal article
Published: 11 April 2018 in Water
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Vietnam is highly vulnerable to flood and storm impacts. Holistic flood risk assessment maps that adequately consider flood risk factors of hazard, exposure, and vulnerability are not available. These are vital for flood risk preparedness and disaster mitigation measures at the local scale. Unfortunately, there is a lack of knowledge about spatial multicriteria decision analysis and flood risk analysis more broadly in Vietnam. In response to this need, we identify and quantify flood risk components in Quang Nam province through spatial multicriteria decision analysis. The study presents a new approach to local flood risk assessment mapping, which combines historical flood marks with exposure and vulnerability data. The flood risk map output could assist and empower decision-makers in undertaking flood risk management activities in the province. Our study demonstrates a methodology to build flood risk assessment maps using flood mark, exposure and vulnerability data, which could be applied in other provinces in Vietnam.

ACS Style

Chinh Luu; Jason Von Meding. A Flood Risk Assessment of Quang Nam, Vietnam Using Spatial Multicriteria Decision Analysis. Water 2018, 10, 461 .

AMA Style

Chinh Luu, Jason Von Meding. A Flood Risk Assessment of Quang Nam, Vietnam Using Spatial Multicriteria Decision Analysis. Water. 2018; 10 (4):461.

Chicago/Turabian Style

Chinh Luu; Jason Von Meding. 2018. "A Flood Risk Assessment of Quang Nam, Vietnam Using Spatial Multicriteria Decision Analysis." Water 10, no. 4: 461.

Original paper
Published: 01 November 2017 in Natural Hazards
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The production of flood hazard assessment maps is an important component of flood risk assessment. This study analyses flood hazard using flood mark data. The chosen case study is the 2013 flood event in Quang Nam, Vietnam. The impacts of this event included 17 deaths, 230 injuries, 91,739 flooded properties, 11,530 ha of submerged and damaged agricultural land, 85,080 animals killed and widespread damage to roads, canals, dykes and embankments. The flood mark data include flood depth and flood duration. Analytic hierarchy process method is used to assess the criteria and sub-criteria of the flood hazard. The weights of criteria and sub-criteria are generated based on the judgements of decision-makers using this method. This assessment is combined into a single map using weighted linear combination, integrated with GIS to produce a flood hazard map. Previous research has usually not considered flood duration in flood hazard assessment maps. This factor has a rather strong influence on the livelihood of local communities in Quang Nam, with most agricultural land within the floodplain. A more comprehensive flood hazard assessment mapping process, with the additional consideration of flood duration, can make a significant contribution to flood risk management activities in Vietnam.

ACS Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam. Natural Hazards 2017, 90, 1031 -1050.

AMA Style

Chinh Luu, Jason Von Meding, Sittimont Kanjanabootra. Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam. Natural Hazards. 2017; 90 (3):1031-1050.

Chicago/Turabian Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. 2017. "Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam." Natural Hazards 90, no. 3: 1031-1050.

Journal article
Published: 13 February 2017 in International Journal of Disaster Resilience in the Built Environment
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Purpose One of the main strategic targets in the national power development plan of Vietnam is to give priority to hydropower. However, there is evidence that the most “at risk” in Vietnamese society have, to date, broadly failed to benefit from hydropower development but rather have become more vulnerable. This paper aims to broaden the perspective of decision makers (government agencies, investors and banks) in the hydropower industry regarding the environmental and social impacts of unrestrained development and the critical need to not only reduce disaster risk for communities but also provide a sustainable model for Vietnam’s energy demand. Design/methodology/approach This position paper presents a critique of public policy in Vietnam related to hydropower industry, undertaken alongside an analysis of socio-economic community resilience and disaster risk reduction literature. Findings Small hydropower investment must be delayed until measures are put in place to ensure that multi-stakeholder risk is a central component of the investment dialogue. Current pricing policies are not aligned with the hydropower development management, and this erects barriers to environmentally and socially conscious decision-making. Practical implications This paper suggests that the development of small hydropower projects must be curtailed until new measures are put in place. This has practical implications for investors, policy makers and residents of affected areas. The authors argue for a significant shift in government strategy toward building resilience as opposed to growth and profit at any cost. Social implications Conscious of Vietnam’s energy demands and development goals, this paper investigates the context of increasing disaster risk and ecological pressures, as well as social injustice relating to the hydropower industry. This kind of analysis can support future efforts to reduce disaster risk and the vulnerability of marginalized groups in Vietnam. Originality/value The authors present a comprehensive review of Vietnamese hydropower from a disaster resilience perspective and provide analysis that will be useful in further research in this emerging area.

ACS Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. Balancing costs and benefits in Vietnam’s hydropower industry: a strategic proposal. International Journal of Disaster Resilience in the Built Environment 2017, 8, 27 -39.

AMA Style

Chinh Luu, Jason Von Meding, Sittimont Kanjanabootra. Balancing costs and benefits in Vietnam’s hydropower industry: a strategic proposal. International Journal of Disaster Resilience in the Built Environment. 2017; 8 (1):27-39.

Chicago/Turabian Style

Chinh Luu; Jason Von Meding; Sittimont Kanjanabootra. 2017. "Balancing costs and benefits in Vietnam’s hydropower industry: a strategic proposal." International Journal of Disaster Resilience in the Built Environment 8, no. 1: 27-39.