Rainfall-Earthquake-Induced Landslide Assessment Using a Deep Neural Networks

Ba-Quang-Vinh Nguyen,Yun-Tae Kim 대한토목학회 2020 대한토목학회 학술대회 Vol.2020 No.10

A Hybrid Physical and Machine Learning Model for Assessing Landslide Spatial Probability caused by raising of ground water table and Earthquake in Atsuma, Japan - Case Study

Ba-Quang-Vinh Nguyen,송창호,김윤태 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.8

Landslides are catastrophic natural events primed and/or triggered by extreme rainfalls and strong earthquakes. Simultaneous occurrence of rainfall and seismic activity increases the likelihood of landslides. However, the researchers focused on this aspect are not much. In the present research, a hybrid model was developed to predict the landslide occurrences probability in Atsuma, Japan triggered by rainfalls and earthquakes. The proposed model is a combination of a physical and machine learning model for improving the accuracy of the landslide susceptibility mapping. The proposed model consisted of a physical module, a machine learning module and a matrix approach module. The physical module assessed the effects of rainfall and peak ground acceleration (PGA) on landslide occurrence probability based on a pseudo-static model. The machine learning module applied Multilayer Perceptron Neural Networks to assess landslide susceptibility, using 611 landslide events caused by strong earthquakes and extreme typhoons. The landslide susceptibility maps obtained from these two modules were then combined into final susceptibility map through a matrix approach. The final susceptibility map included five susceptible levels: very low, low, moderate, high, and very high. To evaluate the proposed model performance, the resulting models were assessed using the areas under the receiver operating characteristic curves. The areas under the success rate curves from the physical module, machine learning module and matrix-based approach showed 79.2%, 82.7% and 83.9% accuracy, respectively. Furthermore, the predicted rate curves showed that the areas under the curve for physical module, machine learning module and matrix-based approach were 78.4%, 82.3% and 83.4%, respectively. These results suggest that the proposed hybrid model improves the prediction capability compared to physically-based method or machine learning model and can be readily used to assess spatial probability of landslide.

Rainfall-Earthquake-Induced Landslide Hazard Prediction by Monte Carlo Simulation: A Case Study of mt. Umyeon in Korea

Vinh Ba-Quang Nguyen,김윤태 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.1

Rainfall and earthquakes are two major triggers for landslides. To assess annual rainfall-earthquake-induced landslide hazards, an ensemble model containing three modules: an uncertainty-analysis module, a simulation module and an output module was proposed. In the uncertainty-analysis module, the input parameters including the topography (slope, curvature), soil depth, rainfall, peak ground acceleration and soil physical properties were considered probabilistic rather than taking specific values. A rainfall-earthquake-induced landslide hazard assessment was carried out in the simulation module, which used two separate methods: a pseudo-static model and a Newmark displacement model based on probabilistic data, which were prepared in the uncertainty-analysis module using the Monte Carlo simulation technique. In the output module, the two landslide hazard evaluations were combined into one map. The combined landslide hazard provides a range of annual probabilities of landslide occurrence corresponding to specific confidence levels. The proposed model can be used for reliable forecasting at specific confidence levels.

Landslide Susceptibility Assessment Considering Pore Water Pressure Increases due to Rainfall and Earthquake

( Ba-quang-vinh Nguyen ),( Ji-sung Lee ),( Seung-rae Lee ),( Yun-tae Kim ) 대한지질공학회 2019 대한지질공학회 학술발표회논문집 Vol.2019 No.2

Increased pore-water pressure due to rainfall infiltration and earthquakes is a major cause slope instability. The effect of changes in pore-water pressure on slopes due to rainfall has been considered in many studies, while the generation of pore-water pressure due to seismic loading is often disregarded in earthquake-induced landslide susceptibility assessment. Hence, this study propose a model to assess landslide susceptibility that takes into account increased pore-water pressure during both rainfall and earthquakes. The procedure for the proposed method includes two main steps. In step 1, we analyze the change in the groundwater table due to rainfall infiltration and subsurface flow during rainfall. In step 2, the slope safety factor is calculated using an infinite slope model, considering the generation of excess pore-water pressure under cyclic loading during earthquakes. Landslide susceptibility is established based on the slope factor of safety. We validated the proposed model by analyzing rainfall-earthquake-induced landslide events occurring on September 6, 2018 in Atsuma, Japan. According to our results, the area under the receiver operating characteristic curve of the Atsuma landslide data is 82% and the true-positive rate of unstable slope classification is 98.1%. The proposed model was then applied to Mt. Umyeon, Korea, to assess the rainfall-earthquake-induced landslide susceptibility. Our model classifies the likelihood of landslide occurrence according to four susceptibility levels: high, moderate, low and very low. We also compared our results to those of previous models and show that the proposed approach may provide reasonably accurate predictions of landslide susceptibility during rainfall and earthquake.

Spatial probability assessment of landslide considering increases in pore-water pressure during rainfall and earthquakes: Case studies at Atsuma and Mt. Umyeon

Nguyen, Ba-Quang-Vinh,Lee, Seung-Rae,Kim, Yun-Tae Catena Verlag 2020 Catena Vol.187 No.-

<P><B>Abstract</B></P> <P>Increased pore-water pressure due to rainfall infiltration and cyclic loading is a major cause of slope instability. Many studies have been carried out to assess rainfall-induced landslide spatial probability based on physical models, combining hydrological models to analyze changes in pore-water pressure on slopes due to rainfall. However, the generation of pore-water pressure due to seismic loading is often disregarded during assessments of earthquake-induced landslide susceptibility. Hence, in this paper, we propose a model to assess landslide spatial probability that takes into account increased pore-water pressure during both rainfall and earthquakes. The procedure for the proposed method includes two main steps. In step 1, we analyze the change in the groundwater table due to rainfall infiltration and subsurface flow during rainfall. In step 2, the slope safety factor is calculated using an infinite slope model, considering the generation of excess pore-water pressure under cyclic loading during earthquakes. Landslide spatial probability is established based on the slope factor of safety. We validated the proposed model by analyzing rainfall-earthquake-induced landslide events occurring on September 6, 2018 in Atsuma town, Japan. According to our results, the area under the receiver operating characteristic curve of the Atsuma landslide data is 82.4% and the true-positive rate of unstable slope classification is 98.1%. The proposed model was then applied to Mt. Umyeon, Korea, to assess the spatial probability of rainfall-earthquake-induced landslide. Our model classifies the likelihood of landslide occurrence according to four susceptibility levels: high, moderate, low and very low. We also compared our results to those of previous models and show that the proposed approach may provide reasonably accurate predictions of landslide spatial probability during rainfall and earthquake events.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Considering the change in pore-water pressure due to rainfall and earthquake. </LI> <LI> Good performance with real landslide events in Atsuma. </LI> <LI> Cumulative rainfall strongly affect the area of susceptibility class. </LI> <LI> Peak ground acceleration strongly affect the area of susceptibility class. </LI> <LI> Excess pore-water pressure is the main reason for slope instability. </LI> </UL> </P>

Effect of clay fraction and water content on rheological properties of sand-clay mixtures

Nguyen, Vinh Ba Quang,Kang, Hyo-Sub,Kim, Yun-Tae Springer-Verlag 2018 Environmental Earth Sciences Vol.77 No.16

Alpha-Glucosidase Inhibitory Activity of Saponins Isolated from Vernonia gratiosa Hance

Cong Pham Van,Anh Hoang Le Tuan,Vinh Le Ba,Han Yoo Kyong,Trung Nguyen Quang,Minh Bui Quang,Duc Ngo Viet,Ngoc Tran Minh,Hien Nguyen Thi Thu,Manh Hoang Duc,Lien Le Thi,Lee Ki Yong 한국미생물·생명공학회 2023 Journal of microbiology and biotechnology Vol.33 No.6

Species belonging to the Vernonia (Asteraceae), the largest genus in the tribe Vernonieae (consisting of about 1,000 species), are widely used in food and medicine. These plants are rich sources of bioactive sesquiterpene lactones and steroid saponins, likely including many as yet undiscovered chemical components. A phytochemical investigation resulted in the separation of three new stigmastane-type steroidal saponins (1 – 3), designated as vernogratiosides A–C, from whole plants of V. gratiosa. Their structures were elucidated based on infrared spectroscopy  (IR), one-dimensional (1D) and two-dimensional nuclear magnetic resonance (2D NMR), high-resolution electrospray ionization mass spectrometry (HR-ESI-MS), and electronic circular dichroism analyses (ECD), as well as chemical reactivity. Molecular docking analysis of representative saponins with αglucosidase inhibitory activity was performed. Additionally, the intended substances were tested for their ability to inhibit α-glucosidase activity in a laboratory setting. The results suggested that stigmastane-type steroidal saponins from V. gratiosa are promising candidate antidiabetic agents.