## 1 Introduction
Recently, various machine learning methods have been applied to landslide susceptibility assessment, using techniques such as logistic regression, support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and deep neural networks (DNN), to improve the accuracy of predictions [11; 12]. [PERSON] et al. [13] compared the performance of heuristic models, mathematical statistical models, and machine learning models in landslide susceptibility assessment. The results showed that machine learning models have higher accuracy. According to the \"2020 Research Fronts\" report, using machine learning methods to study landslide susceptibility has been listed as one of the top ten research hotspots in the field of Earth Sciences [14]. Previous research has extensively reviewed landslide susceptibility assessments. [PERSON] et al. [15], through a statistical analysis of 565 papers from 1985 to 2016, found a growing inclination toward machine learning methods in landslide susceptibility assessments. The authors provided recommendations for study preparation, evaluation, and model usage based on the literature and existing experience. [PERSON] et al. [16], by examining 536 papers from 2001 to 2020, recognized the impact of land use and land use changes on landslide susceptibility assessments. [PERSON] et al. [17] summarized the machine learning models used for landslide susceptibility assessment and identified the latest trends. Their comprehensive study of the performance of machine learning models revealed that tree-based ensemble algorithms, particularly the random forest model, achieved superior results in accurate landslide susceptibility assessments. [PERSON] et al. [18] reviewed common methods for landslide inventory, evaluation indices, evaluation units, models, and verification techniques, detailing their advantages and disadvantages, and identified the current shortcomings in each area. They also discussed the research challenges in terms of spatial scale, qualitative and quantitative issues, and the spatial representation of landslide information. Compared to existing studies, this paper delves deeper into uncertainties in the machine learning model selection process for landslide susceptibility assessment. Specifically, we focus on how to choose appropriate machine learning models and evaluation factors to enhance prediction accuracy. We also propose improved methods for selecting non-landslide points to address the reliability issues caused by random selection. Additionally, our research pays special attention to the interpretability and transferability of machine learning models, aspects that have been less fully explored in other studies. By thoroughly examining the construction of evaluation indicator systems and addressing spatial heterogeneity, we provide new perspectives and directions for future research, aiming to tackle key challenges in applying machine learning to landslide susceptibility assessment. Overall, our study not only enriches the theoretical foundation of landslide susceptibility evaluation but also suggests a series of specific improvements and strategies to better address complex issues in practical applications, thereby offering more reliable and effective tools for landslide risk management. The researchers employed \"machine learning\" and \"landslide susceptibility assessment\" as key search terms, leading to a comprehensive review of 146 related papers published up to 2024. These articles were meticulously gathered from reputable databases, specifically, the Web of Science (WOS) and the China National Knowledge Infrastructure (CNKI). Through a manual selection process, each paper was evaluated for its relevance and contribution to the field, ensuring a robust representation of recent advancements in the application of machine learning techniques for assessing landslide susceptibility.