# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
###### Abstract
When creating landslide susceptibility maps, two factors are considered extremely important: (a) high-quality and sufficiently available data and (b) the application of appropriate predictive models [4]. Currently, there are a multitude of research methods which present a variety, in terms of the approach followed, and are constantly updated. Landslide susceptibility mapping is a complex nonlinear problem; therefore, in most cases, traditional statistical methods are considered inadequate for this task [9]. Methods such as the statistical index (SI), frequency ratio (FR), evidence-based belief functions (EBFs), and certainty factors (CFs) tend to yield less accurate predictions compared with the more advanced machine learning and hybrid models that have been introduced in recent years [10; 11; 12]. In this context, machine learning methods are playing an increasingly important role in the study of natural hazards, not just for landslide susceptibility assessments, but also in broader domains where predictive modeling and risk evaluation are critical [13]. Recent advances in performance-based hazard analysis have underscored the importance of selecting optimal intensity measures to enhance the resilience of underground infrastructure under seismic conditions. For example, a recent study by [PERSON] et al. (2025) [14] demonstrated that sustained maximum acceleration is the most effective indicator for probabilistic seismic demand analysis of shield tunnels in both liquefiable and non-liquefiable soils, outperforming traditional measures such as peak ground acceleration and Housner spectral intensity. This insight aligns with our emphasis on identifying key conditioning factors in landslide-prone regions, where precise hazard quantification is essential for improving infrastructure resilience and disaster preparedness strategies [14]. Using machine learning algorithms, the probability of the occurrence of potential landslides in the study area can be determined by analyzing the spatial relationship between the landslide hazards that have occurred and the conditioning factors that induced landslides. In recent years, several machine learning methods have achieved remarkable results in landslide susceptibility research, such as artificial neural networks (ANNs), J48 decision trees (JDTs), bagging, and Logistic Model Trees (LMTs) [13; 14; 15; 16; 17; 18]. Furthermore, the four machine learning algorithms used in this article are also widely used. For instance, [PERSON] et al. used different machine learning models, namely, Naive Bayes (NB), Multi-Layer Perceptron (MLP), and Functional Trees (FTs), to map the susceptibility of landslides in Uttarakhand Area, India [19]. Similarly, [PERSON] et al. used Credal Decision Tree (CDT)and Radial Basis Function Network (RBFN) models and their hybrid models to conduct a comparative study on the susceptibility to landslides in Nanchuan County, China [20]. In another study, [PERSON] et al. applied the Random Forest (RF) method to the evaluation of the susceptibility to shallow landslides in Giampilieri (in the northeast of Sicily, Italy) and conducted a comparative study with Logistic Regression (LR) [21]. Additionally, [PERSON] et al. applied NB and Bayesian network models to landslide prediction research [22]. Each machine learning model has distinct characteristics, and its performance is often influenced by the specific attributes of the study area and the reliability of the data input [13; 23]. This study aimed to advance landslide susceptibility assessment by applying four machine learning models--MLP, NB, CDT, and RF--to Mei County. These models span a range of complexities, from probabilistic (NB) and tree-based (CDT and RF), to neural network-based (MLP), allowing us to explore how different algorithmic approaches handle spatial and geomorphological variability. Our choice was further guided by findings in existing literature where these models have demonstrated reliable performance in complex terrain and data-limited contexts.