Landslide Susceptibility Analysis in Baekdu Mountain Area Using ANN and AHP Method

Hechun Quan,Hongduk Moon,Guangri Jin,Sungsik Park 한국지반환경공학회 2014 한국지반환경공학회논문집 Vol.15 No.12

To analyze the landslide susceptibility in Baekdu mountain area in china, we get two susceptibility maps using AcrView software through weighted overlay GIS (Geographic Information System) method in this paper. To assess the landslide susceptibility, five factors which affect the landslide occurrence were selected as: slope, aspect, soil type, geological type, and land use. The weight value and rating value of each factor were calculated by the two different methods of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network). Then, the weight and rating value was used to obtain the susceptibility maps. Finally, the susceptibility map shows that the very dangerous areas (0.9 or higher) were mainly distributed in the mountainous areas around JiAnShi, LinJiangShi, and HeLongShi near the china-north Korea border and in the mountainous area between the WangQingXian and AnTuXian. From the contrast two susceptibility map, we also Knew that The accuracy of landslide susceptibility map drew by ANN method was better than AHP method.

Landslide Susceptibility Analysis in Baekdu Mountain Area Using ANN and AHP Method

Quan, Hechun,Moon, Hongduk,Jin, Guangri,Park, Sungsik Korean Geo-Environmental Society 2014 한국지반환경공학회논문집 Vol.15 No.12

To analyze the landslide susceptibility in Baekdu mountain area in china, we get two susceptibility maps using AcrView software through weighted overlay GIS (Geographic Information System) method in this paper. To assess the landslide susceptibility, five factors which affect the landslide occurrence were selected as: slope, aspect, soil type, geological type, and land use. The weight value and rating value of each factor were calculated by the two different methods of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network). Then, the weight and rating value was used to obtain the susceptibility maps. Finally, the susceptibility map shows that the very dangerous areas (0.9 or higher) were mainly distributed in the mountainous areas around JiAnShi, LinJiangShi, and HeLongShi near the china-north Korea border and in the mountainous area between the WangQingXian and AnTuXian. From the contrast two susceptibility map, we also Knew that The accuracy of landslide susceptibility map drew by ANN method was better than AHP method.

Landslide Susceptibility Evaluation in Yanbian Region

Xiuxuan Liu,Hechun Quan,Hongduk Moon,Guangri Jin 한국지반환경공학회 2017 한국지반환경공학회논문집 Vol.18 No.2

In order to evaluate landslide susceptibility in Yanbian region, this study analyzed 7 factors related to landslide occurrence, such as soil, geology, land use, slope, slope aspect, fault and river by Analytic Hierarchy Process (AHP), and calculated the weights of these 7 hazard-induced factors, determined the internal weights and the relative weights between various factors. According to these weights, combining the Remote Sensing technology (RS) with Geographic Information System technology (GIS), the selected area was evaluated by using GIS raster data analysis function, then landslide susceptibility chart was mapped out. The comprehensive analysis of AHP and GIS showed that there has unstable area with the potential risk of sliding in the research area. The result of landslide susceptibility agrees well with the historical landslides, which proves the accuracy of adopted methods and hazard-induced factors.

Landslide Susceptibility Evaluation in Yanbian Region

Liu, Xiuxuan,Quan, Hechun,Moon, Hongduk,Jin, Guangri Korean Geo-Environmental Society 2017 한국지반환경공학회논문집 Vol.18 No.2

In order to evaluate landslide susceptibility in Yanbian region, this study analyzed 7 factors related to landslide occurrence, such as soil, geology, land use, slope, slope aspect, fault and river by Analytic Hierarchy Process (AHP), and calculated the weights of these 7 hazard-induced factors, determined the internal weights and the relative weights between various factors. According to these weights, combining the Remote Sensing technology (RS) with Geographic Information System technology (GIS), the selected area was evaluated by using GIS raster data analysis function, then landslide susceptibility chart was mapped out. The comprehensive analysis of AHP and GIS showed that there has unstable area with the potential risk of sliding in the research area. The result of landslide susceptibility agrees well with the historical landslides, which proves the accuracy of adopted methods and hazard-induced factors.

Assessment of Landslide Susceptibility using the PCA and ANFIS with Various Metaheuristic Algorithms

Zelu Chen,Hechun Quan,Ri Jin,Aifen Jin,Zhehao Lin,Guangri Jin,Guang-Zhu Jin 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.4

It is very important for the susceptibility assessment and disaster prediction of the region to effectively evaluate the landslide susceptibility. In this study, Particle Swarm Optimization (PSO), Artificial Bee Colony algorithm (ABC), Shuffled Frog Leaping Algorithm (SFLA) and Bat algorithm (BAT) are used to optimize Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate the landslide susceptibility. 811 sample points were collected through remote sensing analysis and field investigation for susceptibility analysis. Fifteen landslide evaluation factors were quantified and normalized, and the Principal Component Analysis (PCA) method was used to compress them into 6 main factors. The accuracy analysis results of the area under the curve (AUC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) evaluation models show that the AUC values of PSO, ABC, SFLA and BAT are 93.6%, 96.2%, 90.8% and 86.1%, respectively. Among them, the accuracy of ABC is the highest. This study effectively evaluates the landslide susceptibility through a new neural network hybrid method, which provides a theoretical basis for landslide disaster susceptibility management.

동결융해시험에 의한 “CSG” 재료의 장기강도 및 내구 특성

김광일(Guangri Jin),김기영(Kiyoung Kim),문홍득(Hongduk Moon),권혁춘(Hechun Quan) 한국지반환경공학회 2016 한국지반환경공학회논문집 Vol.17 No.12

시공기술의 발전과 더불어 댐 · 제방 건설과 환경문제가 크게 대두되고 있는 실정이다. 최근 여러 국가에서 댐 · 제방 건설시 골재, 시공성, 기초지반에 대한 요구가 상대적으로 높지 않은 CSG(Cemented Sand and Gravel)재료를 활발히 연구, 적용하고 있다. CSG 재료는 시공현장 하상골재, 현장에서 발생하는 암버럭 등을 인위적으로 입도조정하지 않고 최대골재 치수만을 선별하여 소량의 시멘트와 혼합하여 강도증가 및 급속시공이 가능하다. CSG 재료는 인위적인 석산개발 등에 의한 환경파괴를 최소화함으로써 환경부하저감 및 공사비 등의 측면에서 비교적 경제적이며 친환경적이다. CSG 재료의 외부환경은 일반콘크리트가 접하는 수화열환경과는 달리 건습반복, 동결융해 등의 환경에 노출되게 된다. 그러므로 댐 · 제방구조물의 중요성을 감안하여 CSG 재료의 내구성에 대한 연구가 필요하다. 본 연구는 CSG 재료의 내구성에 대하여 고찰하고자 현장채취 CSG 코어재료에 대하여 동결융해 시험을 실시하였다. 시험결과, CSG 재료의 내구성 지수는 시멘트함량 0.4∼0.6kN/㎥의 경우 30∼40, 0.8∼1.0kN/㎥의 경우 40 이상으로 나타났다. 일축압축강도는 0.4∼0.6kN/㎥에서 동결융해 전의 30∼50%, 0.8∼1.0kN/㎥에서 동결융해 전의 40∼70%로 감소하는 것으로 나타났다. 결과적으로 시멘트함량 0.8kN/㎥ 이상의 경우 강도 및 내구성 측면에서 비교적 타당한 것으로 판단된다. With the development of construction technology, constructions of dam and levee (dike) as well as the environmental problems are becoming issues. Recently, many countries have tried to develop and used CSG (Cemented Sand and Gravel), which needs fewer requirements than others in aggregates, constructability and ground condition during the dam construction. Mixing up with small amount of cement, CSG is able to increase the strength and proceed accelerated construction without artificial gradation adjustment of riverbed aggregate and crushed rock on construction site. Thus, CSG can minimize environmental damage resulted from quarries mining and reduce cost of construction. Unlike heat of hydration condition that regular concrete usually met, CSG exposes to repeated dry-wet and freezing and thawing environment. Thus, consider the importance of structure of dam or levee, intensive study on the durability of CSG is needed. In this study, freezing and thawing experiment was carried out to evaluate the durability of CSG. In results, the durability factor of CSG is 30∼40 or >40 when the amount of cement is 0.4∼0.6kN/㎥ or 0.8∼1.0kN/㎥, respectively. The unconfined compressive strength is reduced to 30∼50% or 40∼70% when the amount of cement is 0.4∼0.6kN/㎥ or 0.8∼1.0 kN/㎥, respectively. Taken together, the strength and durability of CSG is reliable when the amount of cement is over 0.8kN/㎥.