Application of Fuzzy Weights of Evidence Method in Landslide Susceptibility Assessment Based on GIS

2013 ◽  
Vol 864-867 ◽  
pp. 2756-2759
Author(s):  
Zhi Wang Wang ◽  
Jian Hua Zhang ◽  
Duan You Li
Keyword(s):  
Landslide Susceptibility ◽  
Research Result ◽  
Slope Angle ◽  
Weights Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Assessment ◽  
Gis Technology ◽  
Landslide Susceptibility Assessment ◽  
Rs And Gis ◽  
Fuzzy Weights

This paper deals with landslide hazards susceptibility assessment in the study area from Zigui to Badong counties in TGP reservoir region using RS and GIS technology. The causative factors including lithology, distance to faults, elevation, slope aspect, slope angle, drainage network, distance to river and distribution of plant are derived from geological map, Digital Elevation Model (DEM) and Spot imagery data using RS and GIS technology. The paper analyzes landslide susceptibility assessment using fuzzy weights of evidence method, which could combine knowledge-based fuzzy membership values with data-based conditional probabilities to improve the accuracy of landslide susceptibility assessment. The research result is very coincident with the occurrence of the known landslides in the study area.

Zonation of the Landslide Hazards Based on Artificial Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 3389-3392
Author(s):  
Zhi Wang Wang ◽  
Duan You Li ◽  
Jing Ning
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Result ◽  
Slope Angle ◽  
Slope Aspect ◽  
Gis Technology ◽  
Rs And Gis ◽  
Landslide Hazards ◽  
Artificial Neural ◽  
Elevation Model

This paper applies RS and GIS technology to study zonation of the landslide hazards in the study area from Badong county to Zigui county in TGP reservoir region. The causative factors involves lithology, distance to faults, slope angle, slope aspect, elevation, drainage network, distance to river and distribution of plant, which are derived from geological map, Spot imagery data and Digital Elevation Model (DEM) based on RS and GIS technology. We analyze the zonation of the landslide hazards with artificial neural network. The research result is very coincident with the occurrence of the known landslides in the study area.

scholarly journals Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

2020 ◽  
Author(s):  
Matebie Meten ◽  
Netra Prakash Bhandary
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Weights Of Evidence ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract Landslide susceptibility assessment is an important tool for disaster management and development activities. Shikoku Island in the southwest Japan is one of the most landslide prone areas due to heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. The objective of this study is to apply Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models in a GIS environment to prepare the landslide susceptibility maps of this area and select the best one for future infrastructure and landuse planning. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were applied and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models resulting two, two and one LSMs respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points was slightly better than FRD and WoE models in predicting the probability of future landslide occurrence.

scholarly journals Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean)

Geosciences ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 493 ◽  
Author(s):  
Vincenzo Marsala ◽  
Alberto Galli ◽  
Giorgio Paglia ◽  
Enrico Miccadei
Keyword(s):  
Indian Ocean ◽  
Landslide Susceptibility ◽  
Integrated Approach ◽  
Geographical Information ◽  
Susceptibility Assessment ◽  
Gis Technology ◽  
Mountain Areas ◽  
Landslide Susceptibility Assessment ◽  
Hydrological Hazards ◽  
Mauritius Island

This work is focused on the landslide susceptibility assessment, applied to Mauritius Island. The study area is a volcanic island located in the western part of the Indian Ocean and it is characterized by a plateau-like morphology interrupted by three rugged mountain areas. The island is severely affected by geo-hydrological hazards, generally triggered by tropical storms and cyclones. The landslide susceptibility analysis was performed through an integrated approach based on morphometric analysis and preliminary Geographical Information System (GIS)-based techniques, supported by photogeological analysis and geomorphological field mapping. The analysis was completed following a mixed heuristic and statistical approach, integrated using GIS technology. This approach led to the identification of eight landslide controlling factors. Hence, each factor was evaluated by assigning appropriate expert-based weights and analyzed for the construction of thematic maps. Finally, all the collected data were mapped through a cartographic overlay process in order to realize a new zonation of landslide susceptibility. The resulting map was grouped into four landslide susceptibility classes: low, medium, high, and very high. This work provides a scientific basis that could be effectively applied in other tropical areas showing similar climatic and geomorphological features, in order to develop sustainable territorial planning, emergency management, and loss-reduction measures.

Susceptibility Assessment of Landslides Caused by Snowmelt in a Typical Loess Area in the Yining County, Xinjiang, China

2021 ◽  
pp. qjegh2021-024
Author(s):  
Yulong Cui ◽  
Junhong Hu ◽  
Jun Zheng ◽  
Gui Fu ◽  
Chong Xu
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Slope Aspect ◽  
Formation Mechanisms ◽  
Susceptibility Assessment ◽  
Visual Interpretation ◽  
Content Type ◽  
Loess Area

Due to the unique climate and frequent human activities, the loess area in Yining County, Xinjiang, China experiences many landslides. In this study, the formation mechanisms and controlling factors of the landslides in a typical loess area in Yining were investigated, including two catastrophic landslides in 2017. Based on 0.6 m-resolution satellite images, the landslides in this area were identified using artificial visual interpretation methods. Nine influencing factors [elevation, slope angle, slope aspect, topographic position index, distance to a road, distance to a river, distance to a fault, rainfall, and normalized difference vegetation index] were selected to assess the landslide susceptibility using a logistic regression (LR) model. Finally, the potential of the LR model for assessing landslide susceptibility was evaluated using a receiver operating characteristic (ROC) curve. The landslide susceptibility assessment results obtained from the LR model are consistent with the actual landslide distribution. The LR model provides a powerful method for assessing the landslides susceptibility in this area. The research methods and results can provide references for the prevention and mitigation of the landslide disasters in the entire Yili Prefecture of China.Thematic collection: This article is part of the Role of water in destabilizing slopes collection available at: https://www.lyellcollection.org/cc/Role-of-water-in-destabilizing-slopes

scholarly journals Landslide susceptibility assessment considering landslide typology. A case study in the area north of Lisbon (Portugal)

2002 ◽  
Vol 2 (1/2) ◽  
pp. 73-82 ◽  
Author(s):  
J. L. Zêzere
Keyword(s):  
Landslide Susceptibility ◽  
Slope Movement ◽  
Information Value ◽  
Slope Instability ◽  
Slope Aspect ◽  
Susceptibility Assessment ◽  
Slope Movements ◽  
Landslide Susceptibility Assessment ◽  
Different Types ◽  
Area North

Abstract. The aim of the study is to confirm the importance of discriminate different types of slope movements for a better landslide susceptibility evaluation. The study was applied to the sample area of Calhandriz (11.3 km2) in the area North of Lisbon. Sixty shallow translational slides, 23 deeper translational movements and 19 rotational movements were selected for statistical analysis. Landslide susceptibility assessment was achieved using a data-driven approach: the Information Value Method (Yin and Yan, 1988). The method was applied both to the total set of considered landslides and to each type of slope movement, and the obtained success rates for the highest susceptibility classes are higher in the latter case. The different types of landslides are not equally conditioned by the considered instability factors. Information scores are higher for lithology, concordance between slope aspect and dip of the strata, and slope angle, respectively, for rotational movements, translational movements and shallow translational slides. The information value of the variables "presence of artificial cut (roads)" and "presence of fluvial channel" is systematically high for the three types of slope movement, pointing out the importance of both anthropogenic influence and bank erosion on slope instability in the study area. Different types of landslides have neither the same magnitude nor equal damaging potential. Furthermore, technical strategies to mitigate landsliding also depend on landslide typology. These are additional reasons to discriminate between different types of slope movements when assessing landslide susceptibility and hazard.

scholarly journals Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale

2017 ◽  
Vol 49 (5) ◽  
pp. 1363-1378 ◽  
Author(s):  
Chengguang Lai ◽  
Xiaohong Chen ◽  
Zhaoli Wang ◽  
Chong-Yu Xu ◽  
Bing Yang
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Model ◽  
Assessment Procedure ◽  
Susceptibility Assessment ◽  
Entropy Weight ◽  
Landslide Susceptibility Assessment ◽  
Basin Scale

Abstract Rainfall-induced landslide susceptibility assessment is currently considered an effective tool for landslide hazard assessment as well as for appropriate warning and forecasting. As part of the assessment procedure, a credible index weight matrix can strongly increase the rationality of the assessment result. This study proposed a novel weight-determining method by using random forests (RFs) to find a suitable weight. Random forest weights (RFWs) and eight indexes were used to construct an assessment model of the Dongjiang River basin based on fuzzy comprehensive evaluation. The results show that RF identified the elevation (EL) and slope angle (SL) as the two most important indexes, and soil erodibility factor (SEF) and shear resistance capacity (SRC) as the two least important indexes. The assessment accuracy of RFW can be as high as 79.71%, which is higher than the entropy weight (EW) of 63.77%. Two experiments were conducted by respectively removing the most dominant and the weakest indexes to examine the rationality and feasibility of RFW; both precision validation and contrastive analysis indicated the assessment results of RFW to be reasonable and satisfactory. The initial application of RF for weight determination shows significant potential and the use of RFW is therefore recommended.

scholarly journals GIS-Based Weights-of-Evidence Model for Landslide Susceptibility Mapping and Assessment in the Hillslopes of Bujumbura, Burundi

2021 ◽  
Author(s):  
Désiré Kubwimana ◽  
Lahsen Ait Brahim ◽  
Abdellah Abdelouafi
Keyword(s):  
Landslide Susceptibility ◽  
Relevant Literature ◽  
Regional Scale ◽  
Slope Angle ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Fault Density ◽  
Evidence Model

Abstract The aim of this research is the modelling of landslide susceptibility in the hillslopes of Bujumbura using the Weights-of-Evidence model, a probabilistic data modelling approach relevant for predicting future landslides at a regional scale. Initially, characteristics and spatial mapping of different landslides type were identified (fall, flow, slide, complex) by thorough interpretation of high-resolution remote sensing data (mountainous areas with difficult access) and intensive fieldwork. Subsequently, the main landslides controlling factors were selected (lithology, fault density, land use, drainage density, slope aspect, curvature, slope angle, and elevation) using in-depth field knowledge and relevant literature. A landslide inventory map with a total of 569 landslide sites was constructed using the data from various sources. Out of those 569 landslide sites, 285 (50.1%) of the data taken before the 2000s was used for training and the remaining 284 (49.9%) sites (post-2000 events) were used for the accuracy assessment purpose. Thereafter, a prediction map of future landslides was generated with an accuracy of 73.7%. The main geo-environmental landslides factors retained are the high density of drainage networks, the lithology often made with weathered gneiss, the high fault density, the steep topography and the convex slope curvature. The landslide susceptibility map validated was reclassified into very high, high, moderate, low and very low zones. The established susceptibility map will allow with the interaction of the real terrain to locate roads, dwellings, urban extension areas, dams located in high landslides risk zones. These infrastructures will require intervention to address their vulnerability with new facilities, slope stabilization, creation of bypass roads, etc. The susceptibility map produced will be a powerful decision-making tool for drawing up appropriate development plans. Such an approach will make it possible to mitigate the socio-economic impacts due to slope instabilities.

scholarly journals LANDSLIDE SUSCEPTIBILITY ASSESSMENT OF KOKAP AREA USING MULTIPLE LOGISTIC REGRESSION

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Somyot Makealoun ◽  
Doni Prakasa Eka Putra ◽  
Wahyu Wilopo
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Research Area ◽  
Multiple Logistic Regression ◽  
Susceptibility Assessment ◽  
Geologic Structure ◽  
Landslide Susceptibility Assessment ◽  
Total Study Area ◽  
Very High

A number of landslides have occured in Kokap SubDistrict, Kulon Progo Regency, Yogyakarta Special Province, Indonesia, which have influenced the communities. The natural disaster is commonly associated with a few days of heavy rainfall events. To mitigate the impact of landslides in this area, a landslide susceptibility assessment needs to be carried out. The main objective of this research is to develop a landslide susceptibility zonation in the research area by applying a logistic regression (LR) method. Field observation was conducted at 68 locations in the research area, in which 46 landslides occured. Data of slope angle, lithology, geologic structure and groundwater conditions were collected. The relationship between landslide occurrence and the slope angle, lithology, geologic structure and groundwater conditions was analysed using the LR method. The analysis results showed a 0.984 standard error, implying a good-fit model. The study area was classified into very low, low, moderate, high and very high landslide susceptibility zones with 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%, respectively, probabilities of occurrence. A 60% area of the total study area was classified as a moderate to very high susceptibility to landslide. From 47 landslides, 80% landslides occured in high and very high landslide susceptibility zones, 17% landslides occured in the moderate susceptibility zone and 2% landslides occured in the low susceptible zone. None of landslides occured in the very low landslide susceptibility zone. The analysis results show that LR method is a very useful method for landslide prediction. Keywords: landslide susceptibility, multiple logistic regression, Kokap Kulon Progo-Indonesia

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