GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam

Luu, Chinh; Pham, Binh Thai; Phong, Tran Van; Costache, Romulus; Nguyen, Huu Duy; Amiri, Mahdis; Bui, Quynh Duy; Nguyen, Luan Thanh; Le, Hiep Van; Prakash, Indra

doi:10.1016/j.jhydrol.2021.126500

Cited by 41 publications

(17 citation statements)

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“…The importance depends on the study area's geo-environmental, climatic, hydrological, and anthropic activities, and on the methodology utilized. In addition, the flood susceptibility model used in this study was data-driven, so the importance of each factor depends on data distribution (Andaryani et al 2021;Luu et al 2021). Importance was calculated by assigning weights to each factor.…”

Section: Discussionmentioning

confidence: 99%

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Nguyen

2022

Journal of Water and Climate Change

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The objective of this study was the development of an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Differential Evolution (DE) to construct flood susceptibility maps in the Ha Tinh province of Vietnam. The database includes 13 conditioning factors and 1,843 flood locations, which were split by a ratio of 70/30 between those used to build and those used to validate the model, respectively. Various statistical indices, namely root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and R1 score, were applied to validate the models. The results show that all the proposed models performed well, with an AUC value of more than 0.95. Of the proposed models, ANFIS-GBO was the most accurate, with an AUC value of 0.96. Analysis of the flood susceptibility maps shows that approximately 32–38% of the study area is located in the high and very high flood susceptibility zone. The successful performance of the proposed models over a large-scale area can support local authorities and decision-makers develop policies and strategies to reduce the threats related to flooding in the future.

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Section: Discussionmentioning

confidence: 99%

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Nguyen

2022

Journal of Water and Climate Change

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“…The selection of these factors is based on the geo-environmental conditions of the study area and literature review of many studies relating to flood susceptibility modeling (Dottori et al, 2018;Gonzalez-Arqueros et al, 2018). The data used in this study were also used in other works (Luu et al, 2021). Figure 4 shows the frequency ratio analysis of the affecting factors with flood inventory of the study area.…”

Section: Flood Influencing Factorsmentioning

confidence: 99%

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Luu

Nguyen

Amiri

et al. 2021

Vietnam J. Earth Sci.

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Floods are among the most frequent highly disastrous hazards affecting life, property, and the environment worldwide. While various models are available to predict flood susceptibility, no model is accurate enough to be used for all flood-prone areas. Model development using different algorithms is a continuous process to improve the prediction accuracy of flood susceptibility. In the study, we used the Radial Basis Function and Fisher’s linear discriminant function to develop a flood susceptibility map for a case study of Quang Binh Province. The model development used ten variables (elevation, slope, curvature, river density, distance from river, geomorphology, land use, flow accumulation, flow direction, and rainfall). For model training and validation, input data was split into a 70:30 ratio according to flood locations. Statistical indexes were used to evaluate model performance such as Receiver Operating Characteristic, the Area Under the ROC Curve, Root Mean Square Error, Accuracy, Sensitivity, Specificity, and Kappa index. Results indicated that the radial basis function classifier model had better performance in predicting flood susceptible areas based on the statistical measures (PPV = 92.00%, NPV = 87.00%, SST = 87.62%, SPF = 91.58%, ACC = 89.50%, Kappa = 0.790, MAE = 0.204, RMSE = 0.292 and AUC = 0.957. Therefore, the radial basis function classifier algorithm model is appropriate for predicting flood susceptibility in Quang Binh Province.

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“…This environmental change raises concerns because the increment in the frequency and magnitude of extreme rainfall events will result in higher flood risk [1]. As Luu et al [6] reported, the Indian Himalaya in 2013 witnessed the terrible event of the Kedarnath flash flood, which was very disastrous and tragic, resulting in a serious stage modification of some geomorphological units in the watershed.…”

Section: Introductionmentioning

confidence: 99%

“…Floods occur when the quantity of precipitation is greater than the capacity of reservoirs and river channels [6]. In the coastal areas, floods develop because of the tropical cyclones and tsunamis' impact due to the ripples of waves [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Attention must be given to accuracy in the construction of models for predicting and mapping flood susceptibility in urban areas. It is only an accurate model that will guarantee the formation of efficient and effective flood mitigation and control measures [6]. Significantly, remote data gathering and computerized investigation have become vital instruments in recent flood management approaches.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Prediction of Urban Flood Susceptibility from Selected Rivers in a Tropical Catchment Area

Ekwueme

2022

Civ Eng J

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Unexpected flood due to climate change has caused tremendous damage to both lives and properties, especially in tropical areas. Nigeria Southeastern region has been devastated by flood from extreme weather conditions. Flood mitigation involves accurate forecasting, precise prediction, evaluation, and intervention strategy. This study aims at using machine learning solutions to investigate and predict flood susceptibility from selected rivers in the south-eastern region of Nigeria. The regional hydrogeological data from 1981–2019 was collected and analysed. The remote sensing datasets from the National Aeronautics and Space Administration (NASA), Modern-Era Retrospective Analysis for Research and Applications (MERRA) version 2 & 3 platforms from five selected rivers were processed. With the data output of the hydrology, streamline flows, and exposed geology, the ARIMA model was built and used to forecast the flood. The result shows that the flooding pattern would increase by 15-150% within 2020-2024. The forecast indicated that within five years, the river discharge for Adada, Ajali, Imo, Ivo, and Otanmiri will increase within ranges 200-702 m3s-1, 16-26 m3s-1, 508-1280 m3s-1, 43-68.5 m3s-1, and 22-35.1 m3s-1 respectively. Climate change has impacted severely on flood in the region. This knowledge will help the regional agencies and authorities in adapting to flood innuendoes and assessment of hydrologic extremes. Doi: 10.28991/CEJ-2022-08-09-08 Full Text: PDF

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GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam

Cited by 41 publications

References 75 publications

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Machine Learning Based Prediction of Urban Flood Susceptibility from Selected Rivers in a Tropical Catchment Area

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