A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

Bui, Dieu Tien; Hoang, Nhat‐Duc; Martínez‐Álvarez, Francisco; Ngo, Phuong-Thao Thi; Hoa, Pham Viet; Pham, Tien Dat; Samui, Pijush; Costache, Romulus

doi:10.1016/j.scitotenv.2019.134413

Cited by 256 publications

(115 citation statements)

References 58 publications

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“…A range of different geomorphometric and topo-hydrological factors affects flood events, soil erosion, and sediment yield [38][39][40][41][42], and should as such influence site selection for check dams [40]. In assessing the suitability of sites for check dam construction, we considered in this study the range of geomorphometric and topo-hydrological factors listed above (Introduction), based on their previous applications in flood and soil erosion studies (e.g., [43][44][45][46][47]).…”

Section: Considered Geomorphometric and Topo-hydrological Factorsmentioning

confidence: 99%

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

et al. 2019

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Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

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Section: Considered Geomorphometric and Topo-hydrological Factorsmentioning

confidence: 99%

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

et al. 2019

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“…Aspect is defined as the direction of slopes faces [3] which affects the precipitation and solar radiation [40,41]; thus, it affects landslide occurrences [3]. Thus, an aspect map was prepared from a Digital Elevation Model (DEM) with 20 m spatial resolution which was generated from topographical map at the scale of 1: 500,000 ollected from the VIGMR, and classified into nine classes (Figure 3a).…”

Section: Landslide Influencing Factorsmentioning

confidence: 99%

“…Slope is one of the most important factors for landslide incidence [41,[52][53][54]. However, it should be considered in relation with the slope materials to analyze landslide occurrences as the shear resistance of the slope of unconsolidated materials decreases as slope angles increase [3].…”

Section: Landslide Influencing Factorsmentioning

confidence: 99%

“…Rainfall is one of the triggering factors for landslide occurrences in the northern part of Viet Nam including the study region [55,56]. Rainfall decreases the shear resistance of ground/rock mass due to saturation [41,57]. Rainfall map of the study region was generated using rainfall data of 31 years (1984 to 2014) obtained from Global Weather data for SWAT [41,58] and classified into different classes based on annual average rainfall: 3771-4000, 4000-4250, 4250-4500, 4500-4750, 4750-5000, 5000-5250, and 5250-5491 mm (Figure 3j).…”

Section: Landslide Influencing Factorsmentioning

confidence: 99%

“…Rainfall decreases the shear resistance of ground/rock mass due to saturation [41,57]. Rainfall map of the study region was generated using rainfall data of 31 years (1984 to 2014) obtained from Global Weather data for SWAT [41,58] and classified into different classes based on annual average rainfall: 3771-4000, 4000-4250, 4250-4500, 4500-4750, 4750-5000, 5000-5250, and 5250-5491 mm (Figure 3j). Analysis of the FR data indicates that the threshold value of landslide occurrences (FR = 1) is at lower rainfall values (4000-4250 mm), therefore, higher rainfall values are not increasing the landslide events as the slopes already failed at lower values ( Figure 4).…”

Section: Landslide Influencing Factorsmentioning

confidence: 99%

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A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

et al. 2019

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The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas.

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Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

Cited by 256 publications

References 58 publications

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

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