###### Abstract  
Landslide susceptibility assessment is an effective non-engineering landslide prevention at the regional scale. This study aims to improve the accuracy of landslide susceptibility assessment by using an optimized statistical index (SI) method. A landslide inventory containing 493 historical landslides was established, and 20 initial influencing factors were selected for modeling. First, a combination of GeoDetector and recursive feature elimination was used to eliminate the redundant factors. Then, an optimization method for weights of SI was adopted based on Gaussian process regression (GPR). Finally, the predictive abilities of the original SI model, the SI model with optimized factors (GD-SI), and the SI model with optimized factors and weights (GD-GPR-SI) were compared and evaluated by the area under the receiver operating characteristic curve (AUC) on the testing datasets. The GD-GPR-SI model has the highest AUC value (0.943), and the GD-SI model (0.936) also has a higher value than the SI model (0.931). The results highlight the necessity of factor screening and weight optimization. The factor screening method used in this study can effectively eliminate factors that negatively affect the SI model. Furthermore, by optimizing the SI weights through GPR, more reasonable weights can be obtained for model performance improvement. landslide susceptibility; statistical index; Gaussian process regression; GeoDetector; recursive feature elimination +
Footnote †: journal: Medical  
statistical models, and machine learning-based models [5]. Physical-based models are suitable for local-area scale mapping and analysis and have a strong landslide warning ability [6]. However, because of the large amount of required field survey data, the evaluation process is complicated and expensive, making it unsuitable for landslide risk evaluation in large-scale areas [7]. Opinion-driven models such as the analytical hierarchy process [8], step-wise assessment ratio analysis [9], and analytical network process [10] have also been applied in numerous landslide susceptibility studies. In these models, evaluation is based on existing expert knowledge, and the evaluation process does not follow a consistent standard, making quantifying the results difficult. Bivariate statistical models are information mining methods based on statistics, such as frequency ratio (FR) [11; 12], statistical index (SI) [13], certainty factor (CF) [14], and evidence weight [15]. This type of model is straightforward to implement, easy to understand, and has satisfactory prediction performance. More recently, due to the growing development and maturity of big data mining techniques, machine learning has become a hotspot in the field of landslide susceptibility research owing to its powerful data analysis and prediction abilities. In essence, machine learning and multivariate statistical analysis intersect. Further examples including logistic regression (LR) [16; 17], random forest [18; 19; 20], support vector machine [21; 22], artificial neural network [23; 24], and other algorithms, have been applied in landslide susceptibility assessment with advanced prediction performance. In addition to the above models, hybrid methods utilizing multiple model types also achieved excellent performance [25; 26]. Statistical models and machine learning-based models are the most widely used quantitative analysis models. However, both types of models have specific disadvantages. Although machine learning-based algorithms have high predictive accuracy, their underlying rules are complicated and difficult to express intuitively. Hence, they are not conducive to analyzing the relationship between landslides and factors [27; 28]. Bivariate statistical models overcome this problem [29; 30], but they employ a certain irrationality in the distribution of weights, which decreases their predictive accuracy. According to [PERSON]'s first law of geography, objects that are close to each other in geographical space are also more closely related [31].