scholarly journals AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1819
Author(s):  
Tianjun Qi ◽  
Yan Zhao ◽  
Xingmin Meng ◽  
Guan Chen ◽  
Tom Dijkstra
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Human Life ◽  
Relative Weight ◽  
Shallow Landslides ◽  
Mitigation Measures ◽  
Slope Aspect ◽  
Economic Damage ◽  
Geological Conditions

Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Domenico Calcaterra ◽  
Marco Firpo ◽  
Giacomo Pepe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Mitigation Measures ◽  
Economic Losses ◽  
Meaningful Improvement ◽  
Susceptibility Evaluation ◽  
Landslide Runout

<p>Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.</p><p>Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).</p><p>In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.</p>

scholarly journals Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Giuseppe Bausilio ◽  
Domenico Calcaterra ◽  
Pierluigi Confuorto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Geographical Information ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Learning Techniques

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

scholarly journals Investigating causal factors of shallow landslides in grassland regions of Switzerland

2021 ◽  
Vol 21 (11) ◽  
pp. 3421-3437
Author(s):  
Lauren Zweifel ◽  
Maxim Samarin ◽  
Katrin Meusburger ◽  
Christine Alewell
Keyword(s):  
Shallow Landslide ◽  
Shallow Landslides ◽  
Aerial Images ◽  
Slope Aspect ◽  
Related Factors ◽  
Local Conditions ◽  
Causal Factors ◽  
Explanatory Variables ◽  
Individual Site ◽  
Geological Conditions

Abstract. Mountainous grassland slopes can be severely affected by soil erosion, among which shallow landslides are a crucial process, indicating instability of slopes. We determine the locations of shallow landslides across different sites to better understand regional differences and to identify their triggering causal factors. Ten sites across Switzerland located in the Alps (eight sites), in foothill regions (one site) and the Jura Mountains (one site) were selected for statistical evaluations. For the shallow-landslide inventory, we used aerial images (0.25 m) with a deep learning approach (U-Net) to map the locations of eroded sites. We used logistic regression with a group lasso variable selection method to identify important explanatory variables for predicting the mapped shallow landslides. The set of variables consists of traditional susceptibility modelling factors and climate-related factors to represent local as well as cross-regional conditions. This set of explanatory variables (predictors) are used to develop individual-site models (local evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow-landslide points simultaneously. While the local conditions of the 10 sites lead to different variable selections, consistently slope and aspect were selected as the essential explanatory variables of shallow-landslide susceptibility. Accuracy scores range between 70.2 % and 79.8 % for individual site models. The all-in-one model confirms these findings by selecting slope, aspect and roughness as the most important explanatory variables (accuracy = 72.3 %). Our findings suggest that traditional susceptibility variables describing geomorphological and geological conditions yield satisfactory results for all tested regions. However, for two sites with lower model accuracy, important processes may be under-represented with the available explanatory variables. The regression models for sites with an east–west-oriented valley axis performed slightly better than models for north–south-oriented valleys, which may be due to the influence of exposition-related processes. Additionally, model performance is higher for alpine sites, suggesting that core explanatory variables are understood for these areas.

scholarly journals Landslide Susceptibility Zonation Mapping in Post- Earthquake Scenario in Gorkha District

2018 ◽  
Vol 15 ◽  
pp. 45-56 ◽  
Author(s):  
Him Lal Shrestha ◽  
Mahesh Poudel
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landslide Susceptibility ◽  
Remote Sensing Data ◽  
Mitigation Measures ◽  
Ordinal Scale ◽  
Slope Aspect ◽  
Landsat Images ◽  
Earthquake Scenario ◽  
Landslide Susceptibility Zonation

Landslide hazard zonation map is prepared to assist planners to implement mitigation measures so that further damage and loss can be minimized. In this study, post 25 April 2015 earthquake remote sensing data were used to prepare landslide inventory. Landsat images after the earthquake were downloaded from the National Aeronautics and Space Administration (NASA) website and processed using ArcGIS, ERDAS imagine and Analytical Hierarchy Process (AHP) as an extension in ArcGIS. The study was carried out in Gorkha district as this was the epicenter of the main earthquake of 25 April 2015 and consequently was highly affected by earthquake triggered landslide. The digital imagery was processed to analyze land use/land cover type. Geological features were analyzed using the criteria like color, tone, topography, stream drainage, etc. Primary topographic features like slope, aspect, elevation, etc. were generated from Digital Elevation Model (DEM). Seismological data (magnitude and epicenter) were obtained from Department of Seismology. For Landslide Susceptibility Zonation (LSZ) different thematic maps like Land Use and Land Cover (LULC) map, slope map, aspect map, lithological map, buffer map (distance from road and river/water source), soil map, and seismological map were assigned relative weights on the ordinal scale to obtain Landslide Susceptibility Index (LSI). Threshold values were selected according to breaks in LSI frequency and a LSZ map was prepared which shows very low, low, moderate, high, very high hazard zones in Gorkha district.

scholarly journals Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

2021 ◽  
Vol 16 (4) ◽  
pp. 529-538
Author(s):  
Thi Thanh Thuy Le ◽  
The Viet Tran ◽  
Viet Hung Hoang ◽  
Van Truong Bui ◽  
Thi Kien Trinh Bui ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Mountainous Regions ◽  
Geological Conditions ◽  
Very High

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.

scholarly journals Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Trinh Quoc Ngo ◽  
Nguyen Duc Dam ◽  
Nadhir Al-Ansari ◽  
Mahdis Amiri ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Radial Basis Function Network ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Linear Discriminant ◽  
Statistical Measures

Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

scholarly journals Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

2021 ◽  
Vol 13 (16) ◽  
pp. 3281
Author(s):  
Husam A. H. Al-Najjar ◽  
Biswajeet Pradhan ◽  
Bahareh Kalantar ◽  
Maher Ibrahim Sameen ◽  
M. Santosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Input Data ◽  
Vegetation Index ◽  
Slope Aspect ◽  
Gradient Boosting ◽  
Vegetation Density ◽  
Power Functions ◽  
Susceptibility Modeling ◽  
Extreme Gradient Boosting

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).

scholarly journals Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

2020 ◽  
Vol 12 (23) ◽  
pp. 3854 ◽  
Author(s):  
Wei Chen ◽  
Yunzhi Chen ◽  
Paraskevas Tsangaratos ◽  
Ioanna Ilia ◽  
Xiaojing Wang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Evolutionary Algorithms ◽  
Landslide Susceptibility ◽  
Structural Parameters ◽  
Slope Angle ◽  
Slope Aspect ◽  
Support Vector ◽  
Ann Model ◽  
Topographic Wetness Index

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.

scholarly journals The Use of Machine Learning for Accessing Landslide Susceptibility Class: Study Case of Kecamatan Pacet, Kabupaten Mojokerto

2021 ◽  
Vol 884 (1) ◽  
pp. 012006
Author(s):  
Listyo Yudha Irawan ◽  
Sumarmi ◽  
Syamsul Bachri ◽  
Damar Panoto ◽  
Nabila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Research Area ◽  
Google Earth ◽  
Slope Aspect ◽  
Bivariate Analysis ◽  
Class Distribution ◽  
Triggering Factors ◽  
Geological Unit ◽  
Susceptibility Model

Abstract Kecamatan Pacet, Kabupaten Mojokerto is one of an area with many landslide events in East Java Province. As a mitigation effort, this research aimed to map the landslide susceptibility class distribution of the research area. This research applied a machine learning analysis technic which combined Frequency Ratio (FR) and Logistic Regression (LR) models to assess the landslide susceptibility class distribution. FR bivariate analysis is used to normalized the data and to identify the influence significancy on each class of triggering factors. LR multivariate analysis is applied to generate the landslide probability (susceptibility) and to show the influence significancy of each triggering factor to landslide events. There are 12 triggering factors to landslide used in this research, which is: TPI, TWI, SPI, slope, aspect, elevation, profile curvature, distance to drainage, geological unit, rainfall, land use, and distance to the road. This research has 383 landslides and 383 non-landslide events as the data sample based on field survey, BPBD Kabupaten Mojokerto, and Google Earth Pro imagery interpretation. The proportion of dataset training and testing is 70% and 30%, which generated from the data inventory. This research used ROC analysis to validate the landslide susceptibility model. The result showed that the landslide susceptibility model has an AUC value of 0.91, which indicated that the model has high accuracy.

K-Means Cluster Using Rainfall and Storm Prediction in Machine Learning Technique

2019 ◽  
Vol 16 (8) ◽  
pp. 3265-3269
Author(s):  
A. Yovan Felix ◽  
G. S. S. Vinay ◽  
G. Akhik
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heavy Rainfall ◽  
Human Life ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Clustering Problem ◽  
Clustering Model ◽  
Learning Techniques ◽  
Rainfall Flood

Data Mining involves extracting meaningful information from the available data in a user understandable manner. Its role is to analyze voluminous data that is being often assembled. Using the approach of Data mining techniques various business related queries can be attended which formerly were extremely time-consuming to answer. There exist uncontrollable natural disasters that critically hampers and costs human life, environment and revenue material. Natural calamities like heavy rainfall and floods cannot be well predicted until it happens, also it’s beyond one’s power to control them. The aftereffect or destruction caused by these calamities prevails for many years. The term disaster is a result of a vulnerable condition caused by heavy rainfall, flood or storm that can have intense effect at a smaller scale such as a village or at a larger scale such as city or state. Clustering model that was developed before confronted the issue of time complexity, low processing speed and were inappropriate for huge datasets. The current research work proposes the approach of K means clustering that is a subset of ML (machine learning) techniques that are capable to process huge datasets and performs quick computation compared to rest of the clustering model. Various stages in this proposed system include Dataset Collection, Pre-processing, Feature selection, K-means clustering. Among these, the K-means clustering tool which is actually a subset of data-mining and ML approach is employed to cluster observations in the form of groups. It’s a form of unsupervised learning that rectifies the clustering problem. The results reveal that the K-means clustering tool performs clustering faster than the other existing technique.

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