Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling

Luu, Chinh; Ha, Hang; Bui, Quynh Duy; Luong, Ngoc-Dung; Khuc, Dong Thanh; Vu, Hung; Dinh, Nguyen Quoc

doi:10.1016/j.qsa.2023.100083

Cited by 14 publications

(4 citation statements)

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“…Ultimately, these ratios underscored the varying degrees of landslide and flood hazards within the studied region, agreeing with the relative difference between hazard degrees obtained by Luu et al [11]. They also emphasize the importance of tailored disaster-management strategies and land-use planning based on these hazard classifications.…”

Section: Hazard Proportionssupporting

confidence: 89%

“…The cartographic depiction and evaluation of landslide and flash flood hazards constitute a pivotal process in discerning and assessing the vulnerability of a specific geographical area. Landslides, arising from diverse factors such as intense precipitation, seismic activity, slope instability, altitude, aspect, curvature, drainage density, land use and lithology, necessitate a comprehensive analysis to delineate regions susceptible to these events [11]. The resulting landslide hazard maps serve as tools to pinpoint areas at risk of landslides, facilitating the implementation of targeted mitigation strategies [12].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach

Wahba,

El-Rawy,

Al-Arifi

et al. 2023

Water

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Landslides and flash floods are significant natural hazards with substantial risks to human settlements and the environment, and understanding their interconnection is vital. This research investigates the hazards of landslides and floods in two adopted basins in the Yamaguchi and Shimane prefectures, Japan. This study utilized ten environmental variables alongside categories representing landslide-prone, non-landslide, flooded, and non-flooded areas. Employing a machine-learning approach, namely, a LASSO regression model, we generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The LHM identified flood-prone low-lying areas in the northwest and southeast, while central and northwest regions exhibited higher landslide susceptibility. Both LHM and FHM were classified into five hazard levels. Landslide hazards predominantly covered high- to moderate-risk areas, since the high-risk areas constituted 38.8% of the study region. Conversely, flood hazards were mostly low to moderate, with high- and very high-risk areas at 10.49% of the entire study area. The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed by employing the Receiver Operating Characteristic (ROC) curve method, and the Area Under the Curve (AUC) values were determined. The LHM and FHM exhibited an exceptional AUC of 99.36% and 99.06%, respectively, signifying the robust efficacy of the model. The novelty in this study is the generation of an integrated representation of both landslide and flood hazards. Finally, the produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods.

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Section: Hazard Proportionssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach

Wahba,

El-Rawy,

Al-Arifi

et al. 2023

Water

View full text Add to dashboard Cite

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“…Other researchers have also suggested integrating various distinct models into more sophisticated hybrid algorithms with the aim of improving the outcomes and predictive ability of such methods [27]. The research conducted by Chinh Luu et al (2023) strengthens this claim by demonstrating that the hybrid combination of the multiboost algorithm and naive Bayes tree outperformed both multilayer perceptron and support vector machine models [28]. However, as noted by Wolpert and David H (1996), there is no single technique that will produce the best results across all regions [29].…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Ali,

Chen,

et al. 2024

Remote Sensing

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Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

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“…According to a recent survey of loss due to natural disasters, 30-35% of human loss and 50-55% of disaster-damage cost are caused by storms (including flood by storms) [7]. TCs that are active near the coast or landfall will cause torrential precipitation events in association with narrow fields and steep topography, as in Central Vietnam, and this is the primary trigger mechanism for landslide, fast flood, and urban inundation [8,9]. Therefore, it is recognized that TC-induced rainfall forecasts and quantitative TC-induced rainfall forecasts have been considered to be one of the key issues in terms of providing early flood and landslide warnings, preventing disasters, and mitigating flood damage and operating warning systems for TCs.…”

Section: Introductionmentioning

confidence: 99%

Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast

Hung,

Tien,

Quan

et al. 2023

Atmosphere

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This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple radars in Vietnam in ranges up to 6 h were carried out using Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) developed by the Hong Kong Observatory. Secondly, the forecast from the numerical weather prediction (NWP) system, based on the WRF-ARW model running at 3 km horizontal resolution, was blended with radar-based quantitative precipitation estimates and nowcasts of SWIRLS. The analysis showed that the application of the nowcast system to TC-related cloud forms is complicated, which is related to the TC’s evolution and the different types and multiple layers of storm clouds that can affect the accuracy of the derived motion fields in nowcast systems. With hourly accumulated rainfall observation, skill score validation conducted for several TCs that landed in the center of Vietnam demonstrated that the blending of nowcasting and NWP improve the quality of the QPFs of TCs in forecast ranges up to 3 h compared to the pure NWP forecasts.

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Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling

Cited by 14 publications

References 50 publications

A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach

A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast

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