###### Abstract  
This study investigated the performances of different techniques, including random forest (RF), support vector machine (SVM), maximum entropy (maxENT), gradient-boosting machine (GBM), and logistic regression (LR), for landslide susceptibility mapping (LSM) in the rugged terrain of northern Pakistan. Initially, a landslide inventory of 200 samples was produced along with an additional 200 samples indicating nonlandslide areas and divided into training (70%) and validation (30%) groups using a stratified loop-based random sampling approach. Then, a geospatial database of 12 possible landslide influencing factors (LIFs) was generated, including elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), distance to drainage, distance to fault, distance to road, normalized difference vegetation index (NDVI), rainfall, land cover/land use (LCLU), and a geological map of the study area. None of the LIFs were redundant for the modeling, as indicated by the multicollinearity test (tolerance > 0.1) and information gain ratio (IGR > 0). We extended the evaluation measures of each algorithm from area-under-the-curve (AUC) analysis to the calculation of performance overall (POA) with the help of precision, recall, F1 score, accuracy (ACC), and Matthew's correlation coefficient (MCC). The results showed that the SVM was the most promising model (AUC = 0.969, POA = 2669) for the LSM, followed by RF (AUC = 0.967, POA = 2656), GBM (AUC = 0.967, POA = 2623), maxENT (AUC = 0.872, POA = 1761), and LR (AUC = 0.836, POA = 1299). It is important to note that the SVM, RF, and GBM were the top performers, with almost similar accuracy. Thus, each of these could be equally effective for LSM and can be used for risk reduction and mitigation measures in the rugged terrain of Pakistan and other regions with similar topography. machine learning; landslide susceptibility; random forest; support vector machine; gradient-boosting machine; maximum entropy; rugged terrain; Pakistan +
Footnote †: journal: _Sciencedence_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
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Footnote †: journal: _Sciencedencedencedenced_  
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Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedenced_  
Footnote †: journal: _Sciencedencedenced_  
Footnote †: journal: _Sciencedencedencedenced_  
Researchers have proposed various landslide susceptibility mapping (LSM) approaches using satellite remote sensing (SRS) and geographic information systems (GIS) to identify landslide-prone areas [7; 8; 9; 10; 11] by analyzing and evaluating the probability of landslide occurrences [5]. These mapping approaches are divided into two categories, i.e., qualitative and quantitative [1; 12]. The qualitative approaches mostly rely on the level of an expert's knowledge, whereas the quantitative methods are based on probabilistic/statistical theories or models [13]. Both approaches have their limitations, for instance, the misinterpretation of the expert may lower the accuracy in the qualitative methods, whereas the use of poor, low-precision, and inaccurate data may decrease the accuracy in the quantitative methods [14].