Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan

Torizin, Jewgenij; Fuchs, Michael; Awan, Adnan A.; Ahmad, Ijaz; Akhtar, Sardar S.; Sadiq, Simon; Razzak, Asif; Weggenmann, Daniel; Fawad, Faseeh; Khalid, Nida; Sabir, Faisan; Khan, Ahsan Jamal

doi:10.1007/s11069-017-2992-2

Cited by 26 publications

(25 citation statements)

References 32 publications

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“…The Slope and Elevation were the essential landslide parameters, while the influence rate of aspect was the minimum (Figure 8). Therefore, in the research of Dai et al and Ohlmacher et al [54,55], it was found that Elevation and Slope were the best predictor variables for estimating the possibility of landslide events. Xiong et al [53] showed numerous geomorphologic factors (e.g., Elevation) as the maximum significant parameters for predicting the LSM.…”

Section: Important Landslide Variablesmentioning

confidence: 97%

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Landslide Susceptibility Mapping using Machine Learning Algorithm

et al. 2022

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Landslides are natural disasters that have resulted in the loss of economies and lives over the years. The landslides caused by the 2005 Muzaffarabad earthquake heavily impacted the area, and slopes in the region have become unstable. This research was carried out to find out which areas, as in Muzaffarabad district, are sensitive to landslides and to define the relationship between landslides and geo-environmental factors using three tree-based classifiers, namely, Extreme Gradient Boosting (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). These machine learning models are innovative and can assess environmental problems and hazards for any given area on a regional scale. The research consists of three steps: Firstly, for training and validation, 94 historical landslides were randomly split into a proportion of 7/3. Secondly, topographical and geological data as well as satellite imagery were gathered, analyzed, and built into a spatial database using GIS Environment. Nine layers of landslide-conditioning factors were developed, including Aspect, Elevation, Slope, NDVI, Curvature, SPI, TWI, Lithology, and Landcover. Finally, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) value were used to estimate the model's efficiency. The area under the curve values for the RF, XGBoost, and KNN models are 0.895 (89.5%), 0.893 (89.3%), and 0.790 (79.0%), respectively. Based on the three machine learning techniques, the innovative outputs show that the performance of the Random Forest model has a maximum AUC value of 0.895, and it is more efficient than the other tree-based classifiers. Elevation and Slope were determined as the most important factors affecting landslides in this research area. The landslide susceptibility maps were classified into four classes: low, moderate, high, and very high susceptibility. The result maps are useful for future generalized construction operations, such as selecting and conserving new urban and infrastructural areas. Doi: 10.28991/CEJ-2022-08-02-02 Full Text: PDF

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Section: Important Landslide Variablesmentioning

confidence: 97%

“…Therefore, the results of our LSM corroborated the findings of Riaz et al [29] at a certain level. Similarly, Torizin et al [55] created the LSM for the Torghar and Mansehra districts of Pakistan. Their LSM was created by combining four variables: Landcover, Slope, Lithology, and Distance to the fault.…”

Section: Important Landslide Variablesmentioning

confidence: 99%

Landslide Susceptibility Mapping using Machine Learning Algorithm

et al. 2022

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“…Landslides are some of the most fatal natural disasters worldwide, which greatly influences human life and development of infrastructures in underdeveloped countries [1,2]. For instance, about 15,000 landslides happened in China between 2015 and 2017, which caused the economic loss of about one million US dollars.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

et al. 2019

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The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

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“…The high-order accumulation and variable-dimensional fractal are performed between the landslide area and the elevation, which has a good linear relationship in the second-order accumulation and the double logarithmic model with the characteristic scale r, indicating that there is a second-order accumulation between the landslide area and elevation And the variable dimension fractal relationship, its fractal dimension 3.1693 2  D , the correlation coefficient R=0. 9946 [26][27].…”

Section: )Fractal Characteristics Of Landslides and Elevationsmentioning

confidence: 99%

Geological Hazard Prediction of Regional Landslides Based on Geological Clouds and Meteorological Data

2023

IJETC

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Landslide disasters are extremely harmful geological events. The occurrence of landslides will pose a great threat to human life and property safety, and will also cause huge damage to the environment and ecology, restricting human sustainable development. The survey data shows that the proportion of landslide disasters in China ranks first among all geological disasters, accounting for 74%. Therefore, it is of great economic value and social significance to take necessary and effective landslide disaster assessment studies to effectively predict landslide disasters. After decades of scientific exploration, China has made gratifying results in the study of landslide disasters, but there are still some problems in the quantification of the self-organized criticality of regional geological disasters, frequency and spatial dependence; The selection of evaluation factors in the analysis of landslide disaster susceptibility, the limit of risk assessment and susceptibility analysis also need to continue to explore. The purpose of this article is to predict regional landslide geohazards based on geological clouds and meteorological data. This paper fully absorbs the core idea of Logistical and solves the problem of sample quantification in the process of evaluation and prediction of large regions. Based on the comprehensive advantages of GIS technology and Logistical method, a risk assessment model is established. Taking Xinjiang landslide disaster as an example, the danger of landslide disaster is evaluated. This article concludes that the analysis of the landslide disaster in Xinjiang shows that when the cumulative landslide displacement is between 0. 2 and 0. 4m, the landslide is in a critical state, and an orange warning message is issued. When the cumulative landslide displacement value is greater than 0. 4m, the landslide is in danger. Status, release red warning information.

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Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan

Cited by 26 publications

References 32 publications

Landslide Susceptibility Mapping using Machine Learning Algorithm

Landslide Susceptibility Mapping using Machine Learning Algorithm

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

Geological Hazard Prediction of Regional Landslides Based on Geological Clouds and Meteorological Data

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