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.

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

scholarly journals A New Approach Using AHP to Generate Landslide Susceptibility Maps in the Chen-Yu-Lan Watershed, Taiwan

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 505 ◽  
Author(s):  
Thi Nguyen ◽  
Cheng-Chien Liu
Keyword(s):  
Landslide Susceptibility ◽  
Binary Classification ◽  
Correlation Coefficients ◽  
Pairwise Comparison ◽  
Drainage Density ◽  
Slope Aspect ◽  
Bivariate Analysis ◽  
Susceptibility Map ◽  
Related Factors ◽  
New Approach

This paper proposes a new approach of using the analytic hierarchy process (AHP), in which the AHP was combined with bivariate analysis and correlation statistics to evaluate the importance of the pairwise comparison. Instead of summarizing expert experience statistics to establish a scale, we then analyze the correlation between the properties of the related factors with the actual landslide data in the study area. In addition, correlation and dependence statistics are also used to analyze correlation coefficients of preparatory factors. The product of this research is a landslide susceptibility map (LSM) generated by five factors (slope, aspect, drainage density, lithology, and land-use) and pre-event landslides (Typhoon Kalmaegi events), and then validated by post-event landslides and new landslides occurring in during the events (Typhoon Kalmaegi and Typhoon Morakot). Validating the results by the binary classification method showed that the model has reasonable accuracy, such as 81.22% accurate interpretation for post-event landslides (Typhoon Kalmaegi), and 70.71% exact predictions for new landslides occurring during Typhoon Kalmaegi.

scholarly journals Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

2019 ◽  
Vol 58 ◽  
pp. 163-171 ◽  
Author(s):  
Arishma Gadtaula ◽  
Subodh Dhakal
Keyword(s):  
Landslide Susceptibility ◽  
Google Earth ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Mountainous Area ◽  
High Susceptibility ◽  
Seismic Landslides ◽  
2015 Gorkha Earthquake ◽  
Very High

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

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.

The Use of LiDAR DTM in Landslide Susceptibility/Hazard Analysis

2020 ◽  
Author(s):  
Chyi-Tyi Lee ◽  
Tsung-Chi Ji
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Hazard Analysis ◽  
Total Curvature ◽  
Measurement Scale ◽  
Slope Aspect ◽  
Related Factor ◽  
Optimal Measurement ◽  
Susceptibility Model ◽  
Landslide Distribution

<p>High-resolution DTM does not always help build a good landslide prediction model. When we are using LiDAR DTM in producing a topographic-related factor for grid-based landslide susceptibility/hazard analysis, the selection of an optimal measurement scale becomes important. Because the resolution of LiDAR DTM may be up to 1 meter, and the average landslide size may be more than 1 thousand square meters, to use a conventional 3x3 kernel for calculation of a factor value is not valid. Actual tests tell us, to use a 15x15 and larger kernel for calculation may yield a more effective factor for interpreting the landslide distribution in a study area.</p><p>A test area was selected at the catchment of the Zengwen Reservoir in southwestern Taiwan. The original 1mx1m LiDAR DTM was firstly reduced to a 2mx2m DTM for analysis. Factors of slope gradient, slope aspect, topographic roughness, slope roughness, plan curvature, profile curvature, tangential curvature and total curvature are analyzed by using a series of kernels in different sizes up to 25x25 for comparison. And success rate curve method was used to evaluate the effectiveness of each factor in interpreting landslide distribution. Highest AUC is selected as the most effective one and the kernel size which yield that is the optimal measurement scale of the factor.</p><p>A 3x3 kernel has a measurement scale of 2h and is 4 meters (h is grid size of 2 meters), a 25x25 kernel has a measurement scale of 24h and is 48 meters. Factors calculated from an optimal measurement scale will be selected for construction of a landslide susceptibility model. The success rate and prediction rate of this model would be significantly increasing as compared with the model built from conventional 3x3 kernel calculated factors. Finally this optimal susceptibility model was used to construct a landslide hazard model for prediction of landslide distribution under different triggering events.</p>

Dormant and Active Landslides Classification Using Machine Learning Algorithms Coupled With Geological Field Inspection: Pohang Case Study

2020 ◽  
Author(s):  
Omar F. Althuwaynee ◽  
In-Tak Hwang ◽  
Hyuck-jin Park ◽  
Swang-Wan Kim ◽  
Ali Aydda
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Field Work ◽  
Google Earth ◽  
Aerial Photographs ◽  
Susceptibility Map ◽  
Alluvial Deposits ◽  
Basic Field ◽  
Entire Study ◽  
Extreme Gradient Boosting

<p>In 1998, intense rainfall events hit the Pohang state, south west of Korea, which results in highest number of landslides registered in this area (generally the area has a relatively short history of landslide inventorying). The current inventory was digitized using Aerial photographs (lack of photogeological stereoscopic analysis of the aerial images) and coupled with basic field verification (due to limit funding available). Leaving the applied susceptibility maps models performed, using this inventory, with high degree of uncertainty.  Currently a research initiative carried to audit the landslide inventory using freely available aerial photographs and the time tuning function in Google earth for aerial archives. We notice some slopes area covered with deformed forest types that is similar in texture to that seen in digitized locations of landslides inventory. Due to long retune period of similar rainfall event, and with an assumption that the available landslides inventory might not complete. A certain hypothesis of additional investigation including field work to audit the landslides incidents is highly needed. In the current research, we assumed that, some dormant slopes caused by the 1998 event can be reactivated with the current extreme (uncontrolled) uses of slopes by human activities (constructions of real estate’s projects). To that end, a methodology of three main stages were proposed.</p><p>Stage one; Dormant susceptibility map (DSM) coupled with landslide susceptibility map will be produced. Machine learning supervised classification of eXtreme Gradient Boosting algorithms and Ensemble Random Forest, that run on tree-based classification assumption considering only active and dormant landslides as well as stable ground. Stage two; field work needs to be designed by geological and geotechnical experts to collect the doubtful locations by guidance of DSM and consider the new locations as dormant inventory. However, the areas of low dormant susceptibility (or mutual zones with Landslide susceptibility) will be recommended for advanced filed work and soil sampling test to complete the landslides identification of such highly urbanized area. Stage three; knowing the contour depths of diluvial and alluvial deposits can be useful for extracting areas that are more prone to landslides. Especially in the case of a rigid bedrock beneath the diluvial crust. Therefore, reconstructing the Quaternary formation thickness using boreholes repository and then represent the entire study area using CoKriging surface interpolation technique with elevation model. The current research results will provide us a better understanding of landcover stability conditions and their spatial prediction features.</p><div> <div> </div> <div>[email protected]</div> <div>[email protected]</div> </div>

scholarly journals Landslide Susceptibility Region Mapping Using GIS, Analytic Hierarchy Process Model, and Multi-Criteria Analysis at the Chemoga Watershed, Upper Blue Nile, Ethiopia

2021 ◽  
Author(s):  
Hunegnaw Desalegn ◽  
Arega Mulu ◽  
Banchiamlak Damtew
Keyword(s):  
Decision Analysis ◽  
Landslide Susceptibility ◽  
Process Model ◽  
Google Earth ◽  
Slope Aspect ◽  
Human Beings ◽  
Multi Criteria Decision Analysis ◽  
Susceptibility Maps ◽  
Hierarchy Process ◽  
Landslide Susceptibility Maps

Abstract Landslide susceptibility consists of an essential component in the day-to-day activity of human beings. A landslide incident typically happening at a low rate of recurrence when compared and in contrast to other events. This might be generated into main natural catastrophes relating to widespread and undesirable sound effects. Therefore, based on this perception and merging with the expert approach that has been the propensity to encourage and defy extreme physical development to generate a methodology to use GIS, AHP, and Multi-criteria decision analysis for landslide susceptibility maps. A geographic information system is a grouping with additional methods, such as the method for multi-criteria decision making. MCDA techniques are applied under such circumstances to categorize and class decisions for successive comprehensive estimation or else to state possible from impossible potentiality with various landslides. Analytical Hierarchy Process (AHP) constructively applies for conveying influence to different criteria within Multi-criteria decision analysis. The causative landslide weights utilized within this research were elevation, slope, aspect, Soil type, Lithology, Distance to stream, Land use land cover, rainfall and drainage density achieved from various sources. Subsequently, to explain the significance of each constraint into landslide susceptibility weights of all factors were finding out AHP technique. Generally, landslide susceptibility maps of all factors were multiplied to their weights to acquire with the AHP technique. The result showed that the AHP methods are comparatively good quality estimators of landslide susceptibility identification within the chemoga watershed. As the result, the Chemoga watershed area of landslide susceptibility map classes was classified as 46.52%, 13.83%.18.71%, 15.39%, and 5.55% of the occurred landslide fall to very low, low, moderate, high, and very high susceptibility zones respectively. Performance and accuracy of modeled maps have been established using GPS field data and Google earth data landslide map and Area under Curve (AUC) of the Receiver Operating Characteristic curve (ROC). The research outcomes inveterate the very good test consistency of the generated maps. As the result, validation depends on the ROC specifies the accuracy of the map formed with the AHP merged through weighted overly method illustrated very good accuracy of AUC value 81.45%.

scholarly journals Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal

2021 ◽  
Vol 11 (1) ◽  
pp. 167-177
Author(s):  
Niraj Baral ◽  
Akhilesh Kumar Karna ◽  
Suraj Gautam
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Satellite Image ◽  
Image Interpretation ◽  
Google Earth ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Prediction Rate ◽  
Using Data

Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.

scholarly journals Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

2020 ◽  
Vol 17 (14) ◽  
pp. 4933 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ayub Mohammadi ◽  
Himan Shahabi ◽  
Baharin Bin Ahmad ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Predictive Value ◽  
Vegetation Index ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

scholarly journals Landslide susceptibility in Rasuwa District of central Nepal after the 2015 Gorkha Earthquake

2019 ◽  
Vol 59 ◽  
pp. 79-88 ◽  
Author(s):  
Badal Pokharel ◽  
Prem Bahadur Thapa
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Training Dataset ◽  
Slope Aspect ◽  
Gorkha Earthquake ◽  
Central Nepal ◽  
Prediction Rate ◽  
2015 Gorkha Earthquake ◽  
High Resolution Satellite Images

The 2015 Gorkha Earthquake (7.8 Mw) triggered several landslides in central Nepal with major damages in 14 districts. Among them, the Rasuwa district at the north of Kathmandu Valley faced severe landslides due to rugged topography, complex geology and improper land use development. The landslides had blocked the Pasang Lhamu Highway and dammed the Trishuli River at many places. A total of 1416 landslide locations were detected in the district from high resolution satellite images in Google Earth. In this study, landslide susceptibility was modeled in the Rasuwa District by considering slope, aspect, elevation, geology, peak ground acceleration (PGA), land use, drainage proximity and thrust proximity as the predictive factors for landslide occurrences. The landslide inventory was split into 70% and 30% portions as the training dataset and testing dataset respectively. The results from modified frequency ratio (FR) suggest that effect of geology with prediction rate 2.52 is the highest among all factors and is followed by elevation (2.38) and drainage proximity (2.12). The results were verified using area under curve (AUC) and the prediction rate was found to be 79.14%. The computed landslide susceptibility map is helpful for land use planning and landslide risk reduction measure in the Rasuwa District.

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