scholarly journals COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR

2017 ◽  
Vol 23 (1) ◽  
pp. 155-172
Author(s):  
Ricardo Ríos ◽  
Alexandre Ribó ◽  
Roberto Mejía ◽  
Giovanni Molina
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
El Salvador ◽  
Hazard Assessment ◽  
Slope Angle ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Landslide Hazard Assessment ◽  
San Salvador ◽  
Geostatistical Approach

This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

scholarly journals Landslide hazard assessment around MCT zone in Marsyangdi River basin, west Nepal

2017 ◽  
Vol 53 ◽  
pp. 93-98
Author(s):  
Subash Acharya ◽  
Dinesh Pathak
Keyword(s):  
Logistic Regression ◽  
River Basin ◽  
Hazard Assessment ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Map ◽  
Landslide Hazard Assessment ◽  
Hazard Zone ◽  
High Hazard ◽  
Landslide Hazard Map

In the hilly and mountainous terrain of Nepal, landslide is the most common natural hazard especially during prolong rainfall. Every year landslide cost lives and causes injuries. In order to address this problem, the best that can be done is to prepare the landslide hazard map of the area, apply mitigation measures and evacuate the high hazardous area, if necessary. Landslide hazard assessment is the primary tool so as to understand the nature and characteristics of the slope that are prone to failure. Logistic Regression Model is used for the preparation of landslide hazard map of the Besi Shahar-Tal area in Marsyangdi River basin in west Nepal. The causative factors such as elevation, slope, slope aspect, land use, geology, rainfall, lineament density, stream density are used. All the thematic layers of these parameters are prepared in GIS and logistic regression analysis is done by using Statistical Package for Social Science (SPSS). Five different hazard zones are separated namely very low hazard zone, low hazard zone, medium hazard zone, high hazard zone and very high hazard zone. The high hazard zone is lying along the Marsyangdi River and its tributaries.

Structuring a Bayesian belief network using expert knowledge for landslide hazard assessment

2021 ◽  
Author(s):  
Shreyasi Choudhury ◽  
Bruce D. Malamud ◽  
Amy Donovan
Keyword(s):  
Hazard Assessment ◽  
Expert Knowledge ◽  
Grey Literature ◽  
Slope Angle ◽  
Bayesian Belief Network ◽  
Landslide Hazard ◽  
Small Scale ◽  
Landslide Hazard Assessment ◽  
Belief Network ◽  
Darjeeling Himalayas

<p>Landslide hazard assessment in India using historical data faces three challenges: (i) difficulty of obtaining systematic landslide occurrence data; (ii) under-representation of small-scale landslides; (iii) lack of recording of the physical/anthropogenic influences on landsliding. Here we show development of a Bayesian Belief Network (BBN) for a multi-hazard landslide assessment using experts’ judgements. Experts were chosen based on their experience on landslides and/or in Darjeeling Himalayas. A BBN produces a probability estimation of possible events and is a graph containing a set of variables (nodes) and conditional (in)dependencies between the nodes (arcs).</p><p>To better understand the relative weighting of potential causes of landslides in our case study area -Darjeeling Himalayas- we carried out four steps. (<strong>Step 1</strong>) We reviewed 29 peer- and grey-literature sources to list 13 physical/anthropogenic variables that might influence landsliding. (<strong>Step 2</strong>) We interviewed 11 experts about the importance of these 13 variables and asked for additional potential variables (resulting in 35 variables). (<strong>Step 3</strong>) We used interviews plus questionnaire to ask 16 experts to rate each of the 35 variables (scale 1-10) as to their potential to influence landsliding. The experts also added 7 more variables (resulting in 46 variables). (<strong>Step 4</strong>) Based on the ratings and interviews, we chose 35 out of 46 variables as our BBN nodes and from these the BBN arcs. Examples of these variables include rainfall, wildfires, geological weathering, planned infrastructure loading, cultivation (planned/unplanned), railway/road construction changing slope angle (planned), relief, slope, soil cohesion. Based on this study, we found that judgement of local people/academicians/technical experts can be of help whilst developing a BBN structure, allowing us to calculate probabilistic relationships between the nodes in a BBN. This process, therefore, can be utilised for landslide-based multi-hazard assessment in low data regions.</p>

scholarly journals Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province

2021 ◽  
Vol 10 (10) ◽  
pp. 646
Author(s):  
Shenghua Xu ◽  
Meng Zhang ◽  
Yu Ma ◽  
Jiping Liu ◽  
Yong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hazard Assessment ◽  
Vulnerability Assessment ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Shaanxi Province ◽  
Landslide Risk ◽  
Landslide Hazard Assessment ◽  
Landslide Risk Assessment ◽  
Xianyang City

Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.

scholarly journals Landslide Hazard Assessment Based on Bayesian Optimization-Support Vector Machine in Nanping City, China

2021 ◽  
Author(s):  
Wei Xie ◽  
Wen Nie ◽  
Pooya Saffari ◽  
Luis F. Robledo ◽  
Pierre-Yves Descote ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hazard Assessment ◽  
Roc Curve ◽  
Confusion Matrix ◽  
Landslide Hazard ◽  
Assessment Model ◽  
Bayesian Optimization ◽  
Slope Aspect ◽  
Support Vector ◽  
Landslide Hazard Assessment

Abstract Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of model hyperparameters is of great importance to the accuracy and precision of one landslide hazard assessment model. In this study, Bayesian Optimization (BO) method was used to tune the hyperparameters of Support Vector Machine (SVM) model to obtain a high accuracy landslide hazard zoning map. 1711 historical landslide disaster points were obtained as landslide inventory in a case of Nanping City landslide hazard assessment. A total of 12 factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected as landslide conditional factors. The multicollinearity diagnosis was performed on factors using the Spearman correlation coefficient. 1711 landslides and 1711 non-landslides were collected as the dataset and divided into the same number of training dataset and testing dataset. The confusion matrix and receiver operating characteristic (ROC) curve were used to verify the models. The results of confusion matrix accuracy and the area under ROC curve (AUC) showed that BO-SVM (89.53%, 97%) performed better than only SVM (84.91%, 0.93), which indicated the superiority of the proposed method during this study.

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