Application of Data-Driven Techniques For Landslide Susceptibility Prediction Along An Earthquake Affected Road Section in Kashmir Himalaya

2021 ◽  
Author(s):  
Syed Ahsan Hussain Gardezi ◽  
Nadeem Ahmad Usmani ◽  
Xiao-qing Chen ◽  
Nawaz Ikram ◽  
Sajjad Ahmad ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Anthropogenic Activities ◽  
Data Driven ◽  
Weight Of Evidence ◽  
Support Vector ◽  
Operating Characteristics ◽  
Kashmir Earthquake ◽  
Landslide Occurrence ◽  
Statistical Measures ◽  
Road Section

Abstract The interaction of seismic events with geo-environmental conditions and anthropogenic activities may exacerbate the risk of landslide hazard in a mountainous region. As an example of this, 2005 Kashmir earthquake triggered a large number of shallow to deep slope failures, which was further intensified in following years by human activities notably along road networks, posing a long-term hazard. Hence, this study was planned to evaluate the effectiveness of landslide susceptibility prediction along earthquake affected road-section of Neelum Highway using six different data-driven models. We applied analytical hierarchy process as heuristic approach, weight of evidence and index of entropy as statistical models and multi-layer perceptron, support vector machine and binary logistic regression (BLR) as machine learning models. Initially, 224 landslides locations were marked through field surveys to prepare landslide inventory which was further randomly divided into training (70%) and testing (30%) datasets. Then, 13 landslide causative factors (LCFs) were extracted from geo-spatial database and analysed by measuring collinearity among factors and assessing their contribution in landslide occurrence using different feature selection methods for inclusion in susceptibility modelling. Thereafter, six employed models were trained to produced landslide susceptibility maps of investigated road-section. Finally, the area under receiver operating characteristics (AU-ROC) curve and various statistical measures were applied to validate and compare the performance of modeled landslide susceptibility. The results revealed that no collinearity issue exists among all 13 LCFs, and all six models exhibited satisfying performance in predicting landslide susceptibility of study area. However, BLR model have produced most promising and optimum results as compared to other models with AU-ROC (0.881), Matthew’s correlation coefficient (0.609), Kappa coefficient (0.604), accuracy (0.797) and F-score (0.787). The outcomes of this study can be used as pertinent guide for preventing and managing the landslide disaster risk along Neelum Highway and beyond.

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

2020 ◽  
Vol 10 (11) ◽  
pp. 4016 ◽  
Author(s):  
Xudong Hu ◽  
Han Zhang ◽  
Hongbo Mei ◽  
Dunhui Xiao ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Southwest China ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Measures ◽  
Ensemble Machine Learning

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

scholarly journals Landslide susceptibility on selected slopes in Dzanani, Limpopo Province, South Africa

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Makia L. Diko ◽  
Shallati C. Banyini ◽  
Batobeleng F. Monareng
Keyword(s):  
Landslide Susceptibility ◽  
Critical State ◽  
Anthropogenic Activities ◽  
Limpopo Province ◽  
Silty Clay ◽  
Clay Loam ◽  
Landslide Occurrence ◽  
Landslide Event ◽  
Physical Attributes

Inherent soil properties and anthropogenic activities on slope faces are considered potential recipes for landslide occurrence. The objectives of this study were to physically characterise unconsolidated soils and identify on-going anthropogenic activities on selected slopes in Dzanani in order to appraise their role as contributory factors in enhancing landslide susceptibility. Methods employed for this study comprised mapping, description of soil profile, identification of anthropogenic activities, as well as experimental determination of soil colour, particle size distribution and Atterberg limits. Geologically, the study area comprised rocks of the Fundudzi, Sibasa and Tshifhefhe Formations, ascribed to the Soutpansberg Group. Digging of foundations for construction purposes and subsistence agriculture were identified as the main anthropogenic activities. The soils were predominantly reddish-yellow in colour, texturally variable (silty clay – clayey – silty clay loam and clay loam) and of medium plasticity. Compared to soils from other parts of the world developed on volcanic cones or associated with a landslide event, those from Dzanani were qualified as generally inactive and not prone to landslides. Although the physical attributes suggested the soils were not at a critical state, on-going anthropogenic activities may enhance deep weathering and ultimately alter current soil physical characteristics to a critical state.

scholarly journals Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

2021 ◽  
Author(s):  
Tingyu Zhang ◽  
Huanyuan Wang ◽  
Tianqing Chen ◽  
Zenghui Sun ◽  
Tao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Landslide Susceptibility ◽  
Reference Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Support Vector ◽  
Operating Characteristics ◽  
Predisposing Factor

Abstract The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

scholarly journals UTILIZATION OF FINE RESOLUTION SATELLITE DATA FOR LANDSLIDE SUSCEPTIBILITY MODELLING: A CASE STUDY OF KASHMIR EARTHQUAKE INDUCED LANDSLIDES

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 25-30
Author(s):  
M. Z. Ali ◽  
H.-J. Chu ◽  
S. Ullah ◽  
M. Shafique ◽  
A. Ali
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Support Vector ◽  
Kashmir Earthquake ◽  
Validation Data ◽  
Mitigation Measure ◽  
Causative Factors

<p><strong>Abstract.</strong> The 2005 Kashmir earthquake has triggered thousands of landslides which devastated most of the livelihood and other infrastructure in the area. Landslide inventory and subsequently landslide susceptibility mapping is one of the main prerequisite for taking mitigation measure against landslide effects. This study has focused on developing most updated and realistic landslide inventory and Susceptibility mapping. The high resolution data of Worldveiw-2 having spatial resolution of 0.4 m is used for landslide inventory. Support Vector Machine (SVM) classifier was used for landslide inventory developing. Total 51460 number of landslides were classified using semi-automatic technique with covering area of 265 Km<sup>2</sup>, smallest landslide mapped is covering area of 2.01 m<sup>2</sup> and the maximum covered area of single landslide is 3.01 Km<sup>2</sup>. Nine influential causative factors are used for landslide susceptibility mapping. Those causative factors include slope, aspect, profile curvature, elevation, distance from fault lines, distance from streams and geology. Logistic regression model was used for the Landslides susceptibility modelling. From model the highest coefficient was assigned to geology which shows that the geology has higher influence in the area. For landslide susceptibility mapping the 70 % of the data was used and 30% is used for the validation of the model. The prediction accuracy of the model in this study is 92 % using validation data. This landslide susceptibility map can be used for land use planning and also for the mitigation measure during any disaster.</p>

scholarly journals AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 713-720
Author(s):  
G. Karakas ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Natural Phenomenon ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Aerial Photogrammetry ◽  
Prone Area ◽  
Using Data

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.

scholarly journals Assessment of Landslide Susceptibility using Weight of Evidence and Frequency Ratio Model in Shahpur Valley, Eastern Hindu Kush

2021 ◽  
Author(s):  
Ghani Rahman ◽  
Atta Ur Rahman ◽  
Alam Sher Bacha ◽  
Shakeel Mahmood ◽  
Muhammad Farhan Ul Moazzam ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Satellite Image ◽  
High Rate ◽  
Rate Curve ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Contributing Factors ◽  
Landslide Occurrence ◽  
Hindu Kush

Abstract This study assessed landslide susceptibility using Weight of Evidence (WoE) and Frequency Ratio (FR) model in Shahpur valley, situated in the eastern Hindu Kush. Here, the landslides are recurrent phenomena that disrupt the natural environment and causes huge property damages as well as human losses every year. These damages are expected to increase due to the high rate of deforestation in the region, population growth, agricultural expansion and infrastructural development on the slopes. Initially, the landslide inventory map was prepared from the SPOT-5 satellite image and was verified from frequent field visits. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, proximity to roads and streams were selected. To analyze the relationship between landslide occurrence with its causal factors, WoE and FR models were used. Based on WoE and FR model landslide susceptibility zonation maps were prepared and reclassified into very low to very high landslide susceptible zones. Finally, the resultant maps of landslide susceptibility were validated using the success rate curve and prediction rate curve approach to validate the models.

scholarly journals The effects of splitters on the downstream scour hole of overflow spillways: application of support vector regression

2021 ◽  
Author(s):  
Mohammad Ehsan Asadi ◽  
Seyed Taghi Omid Naeeni ◽  
Reza Kerachian
Keyword(s):  
Support Vector Regression ◽  
Experimental Models ◽  
Data Driven ◽  
Support Vector ◽  
Method Performance ◽  
Statistical Measures ◽  
Proposed Model ◽  
Scour Hole ◽  
Scour Hole Dimensions ◽  
Hole Dimensions

Abstract One of the most effective ways to reduce the water jet erosion power during dam overflow is to use splitters on the lower side of spillway. The dimensions of scouring holes and their location in the dam basin should be accurately determined. Experimental models and data driven techniques can be effectively used for estimating the dimensions of scouring holes. The focus of this paper is evaluating the effects of splitters on the downstream scour hole of overflow spillways and providing an optimized splitter configuration. The Support Vector Regression (SVR) method performance in predicting the scour hole dimensions and its location downstream of the dam has been examined using 116 experimental data. In order to evaluate the efficiency of the proposed model, we used different statistical measures. The results show that the presence of splitters decreases the slope of downstream scouring in all situations. It is also shown that the SVR method can accurately estimate the dimensions of the scour hole and its location.

scholarly journals Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2824 ◽  
Author(s):  
Nguyen ◽  
Tuyen ◽  
Shirzadi ◽  
Pham ◽  
Shahabi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Naive Bayes ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Naïve Bayes ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Operating Characteristics ◽  
Conditioning Factors ◽  
Hybrid Intelligence

We proposed an innovative hybrid intelligent approach, namely, the multiboost based naïve bayes trees (MBNBT) method for the spatial prediction of landslides in the Mu Cang Chai District of Yen Bai Province, Vietnam. The MBNBT, which is an ensemble of the multiboost (MB) and naïve bayes trees (NBT) base classifier, has rarely been applied for landslide susceptibility mapping around the world. For the modeling, we selected 248 landslide locations in the hilly terrain of the study area. Fifteen landslide conditioning factors were selected for the construction of the database based on the one-R attribute evaluation (ORAE) technique. Model validation was done using statistical metrics, namely, sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and the area under the receiver operating characteristics curve (AUC). Performance of the hybrid model was evaluated and compared with popular soft computing benchmark models, namely, multiple perceptron neural network (MLPN), Support Vector Machines (SVM), and single NBT. Results indicated that the proposed MBNBT (AUC = 0.824) model outperformed the popular models, namely, the MLPN (AUC = 0.804), SVM (AUC = 0.804), and NBT (AUC = 0.800) models. Analysis of the model results also suggested that the MB meta classifier ensemble model could enhance the prediction power of the NBT model. Therefore, the MBNBT is a suitable method for the assessment of landslide susceptibility in landslide prone areas.

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