A Data-Driven Approach to Landslide-Susceptibility Mapping in Mountainous Terrain: Case Study from the Northwest Himalayas, Pakistan

Riaz, Muhammad Tayyib; Basharat, Muhammad; Hameed, Nasir; Shafique, Muhammad; Luo, Jun

doi:10.1061/(asce)nh.1527-6996.0000302

Cited by 62 publications

(33 citation statements)

References 65 publications

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“…Generally, it is considered that elevation has a firm relationship with landslide occurrence [67]. There is no denying that elevation can influence the topography, vegetation, temperature, humidity, human activities, and many other conditions that have a connection with slope stability [30,68]. In Figure 3c, the elevation of the study area was divided into ten classes with an interval of 200 m, i.e., Plan curvature and profile curvature are two quantitative indices that embody topographic characteristics and trend from different perspectives [69].…”

Section: Data Preparationmentioning

confidence: 99%

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

2019

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The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

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Section: Data Preparationmentioning

confidence: 99%

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

2019

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“…Maximum landslides occurred within the slope range of 30-40°, it is mostly due to local variations in conditions (Ayalew and Yamagishi, 2005), which may include excessive rainfall, human activities including downslope cutting due to highway construction etc. Results show that the maximum landslides in Hunza-Nagar valley, northern Pakistan were found within the slopes having amount of dip ranging from 30°-50° (Bacha et al 2018;Riaz et al, 2018). In terms of aspect, the slope face of maximum landslides was towards south which is probably because of receiving maximum sunshine in daytimes followed by temperature decrease at night times, and this phenomenon helps in weathering processes which provide base for maximum potential slopes.…”

Section: Discussion On Resultsmentioning

confidence: 89%

Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan

Iqbal¹

2021

Acta Geodynamica Et Geomaterialia

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The primary objective of this study is to analyze and characterize landslides in North Pakistan along Karakoram Highway (KKH) to produce a landslide susceptibility map using GIS and remote sensing technology. Using satellite images followed by field investigations, spatial distribution of landslide database was generated. Next, an integrated study was undertaken in the study area to perform the landslide susceptibility mapping. Dubaur-Dudishal section of KKH (about 150 km) which is a part of Kohistan Island Arc, is investigated in this study with a buffer zone of about 8 km along both sides of the KKH. Several thematic maps, e.g., lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI), slope, aspect, elevation, relative relief, plan-curvature and profile-curvature were prepared. Subsequently, these thematic data layers were analyzed by frequency ratio (FR) model and weights-of-evidence (WoE) model to generate the landslide susceptibility maps. In order to check the accuracy of the models, the area under the curve (AUC) was to quantitatively compare the two models used in this study. The predictive ability of AUC values indicate that the success rates of FR model and WoE model are 0.807 and 0.866, whereas the prediction rates are 0.785 and 0.846, respectively. Both methods show that nearly 50 % landslides in the study area fall in either high or very high susceptibility zones. The landslide susceptibility maps presented in this study are of great importance to the policy makers and the engineers for highway construction as well as the mega dams construction projects (Dasu dam and Bhasha dam which lie within the vicinity of the study area); so that proper prevention as well as mitigation could be done in advance to avoid the possible economic as well as the human loss in future.

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“…In this study, the ANN model was created using the 'nntool' in the MATLAB 2016 software. First, the entire dataset, which includes 14 predictors and one target, was divided into a training set (70% or 38 samples) and a test set (30% or 17 samples) as is commonly done in the literature [29][30][31][32]. This step was repeated three times (i.e., Grouping #1, Grouping #2, and Grouping #3) to reduce the data variability to sampling.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

Nguyen

Chen

Lin³

et al. 2020

Sustainability

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This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

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A Data-Driven Approach to Landslide-Susceptibility Mapping in Mountainous Terrain: Case Study from the Northwest Himalayas, Pakistan

Cited by 62 publications

References 65 publications

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

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