Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan

Aslam, Bilal; Maqsoom, Ahsen; Khalil, Umer; Ghorbanzadeh, Omid; Blaschke, Thomas; Farooq, Danish; Tufail, Rana Faisal; Suhail, Salman Ali; Ghamisi, Pedram

doi:10.3390/s22093107

Cited by 19 publications

(9 citation statements)

References 78 publications

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“…These parameters were selected based on the literature [12,[41][42][43][44][45] and are among the standard characteristics used for WQ assessment. The variation inflation factor (VIF) value for all factors is less than 5 and satisfies the maximum threshold [46]. Thus, it can be stated that there is no multicollinearity present among the selected parameters.…”

Section: A Data Collection and Preparationmentioning

confidence: 99%

Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

et al. 2022

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One of the key functions of global water resource management authorities is river water quality (WQ) COD assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques that are renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold crossvalidation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945 and PBIAS = -0.64, outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN-Adaptive Network-Based Fuzzy Inference System (ANFIS).INDEX TERMS Water quality index, machine learning, hybrid data-mining algorithms, cross-validation techniques, North Pakistan

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Section: A Data Collection and Preparationmentioning

confidence: 99%

Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

et al. 2022

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“…Independent variables that denote the landslide presence and absence in the LGR were designated as 1 and 0, respectively. The equation used for LGR is shown below (Hong et al, 2016a;Aslam et al, 2022):…”

Section: Logistic Regressionmentioning

confidence: 99%

“…It reveals how the changing standard deviation of predictors and independent variables changes the dependent variable. The used equation for LR is shown below (Onagh et al, 2012a;Aslam et al, 2022):…”

Section: Linear Regressionmentioning

confidence: 99%

“…Landslides usually occur in those areas where few factors, such as mountains, steep slopes, higher precipitations, and higher seismicity, are substantially found (Hong et al, 2016a;Chen et al, 2018b;Maqsoom et al, 2021;Aslam et al, 2022). Mapping of landslide susceptibility is also of eminent value for land use planning and facilitates planners in making potential development-related decisions (Erener et al, 2016;Bragagnolo et al, 2020;Aslam et al, 2022). Consequently, in current times, appraising different models for landslide susceptibility has become a key research topic globally.…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of machine learning and multi-criteria decision making techniques for landslide susceptibility mapping of Muzaffarabad district

Khalil¹,

Imtiaz

Aslam³

et al. 2022

Front. Environ. Sci.

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Landslides are natural disasters deliberated as the most destructive among the others considered. Using the Muzaffarabad as a case study, this work compares the performance of three conventional Machine Learning (ML) techniques, namely Logistic Regression (LGR), Linear Regression (LR), Support Vector Machine (SVM), and two Multi-Criteria Decision Making (MCDM) techniques, namely Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the susceptibility mapping of landslides. Most of these techniques have been used in the region of Northern Pakistan before for the same purpose. However, this study for landslide susceptibility assessment compares the performance of various techniques and provides additional insights into the factors used by adopting multicollinearity analysis. Landslide-inducing factors considered in this research are lithology, slope, flow direction, fault lines, aspect, elevation, curvature, earthquakes, plan curvature, precipitation, profile curvature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), roads, and waterways. Results show that SVM performs better than LGR and LR among ML models. On the other hand, the performance of AHP was better than TOPSIS. All the models rank slope, precipitation, elevation, lithology, NDWI, and flow direction as the top three most imperative landslide-inducing factors. Results show 80% accuracy in Landslide Susceptibility Maps (LSMs) from ML techniques. The accuracy of the produced map from the AHP model is 80%, but for TOPSIS, it is less (78%). In disaster planning, the produced LSMs can significantly help the decision-makers, town planners, and local management take necessary measures to decrease the loss of life and assets.

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“…Past and current landslide inventory data are a significant factor in predicting landslide potential in a study area (Guzzetti et al, 1995). Therefore, a landslide inventory map is the first mandatory element for generating and compiling authentic LSM of a study area (Aslam et al, 2022a). The inventory map is a significant parameter for performing various quantitative analyses and validating the models' accuracy (Chalkias et al, 2014;Baloch et al, 2021;Baqa et al, 2021;Shah et al, 2021).…”

Section: Landslide Inventory Mapmentioning

confidence: 99%

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

Islam¹,

Riaz

Ghaffar

et al. 2022

Front. Environ. Sci.

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Landslides are a recurrent environmental hazard in hilly regions and affect the socioeconomic development in Pakistan. The current study area is the tourism and hydro energy hub of Pakistan and is affected by environmental hazard. A landslide susceptibility mapping (LSM) of the Hindu Kush Himalayan, Swat District, Pakistan, can be created to reduce demographic losses due to landslides. This current study is conducted to apply three bivariate models, including weights of evidence (WOE), frequency ratio (FR), and information value (IV) for an LSM that has not been explored or applied in the current study area. For this purpose, first, an inventory map of 495 landslides was constructed from both ground and satellite data and randomly divided into training (70%) and testing (30%) datasets. Furthermore, 10 conditioning factors (elevation, slope, aspect, curvature, fault, rainfall, land use land cover (LULC), lithology, road, and drainage) used for the mapping of landslides were prepared in ArcGIS 10.8. Finally, LSM is generated based on WOE, FR, and IV models and validated the performance of LSM models using the area under receiver operating characteristic curve (AUROC). The findings of success rate curve (SRC) of the WOE, FR, and IV models were 67%, 93%, and 64%, respectively, while the prediction rate curves (PRCs) of the three models were 87%, 95%, and 73%, respectively. The validation results for WOE, FR, and IV justified that the FR model is the most reliable technique of all three of these models to produce the highest accuracy LSM for the present study area. Policymakers can use the findings of current research work to mitigate the loss due to landslide hazard.

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Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan

Cited by 19 publications

References 78 publications

Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

Comparative analysis of machine learning and multi-criteria decision making techniques for landslide susceptibility mapping of Muzaffarabad district

Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

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