# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 1 Introduction  
Landslide refers to the geological phenomenon of soil or rock sliding down a slope under the action of gravity [1]. Landslides often occur on steep slopes, and the movement can be either the overall sliding of the slope body or the local soil flow or detachment of rock strata. Landslides are typically triggered by factors such as geological structures, lithology, vegetation cover, rainfall, and earthquakes. Landslides can have severe impacts on nearby residents, transportation, infrastructure, and the environment, making the prevention, monitoring, and management of landslides crucial. The assessment of the probability of landslide occurrence from a spatial perspective, also known as landslide susceptibility assessment (LSA), aims to systematically evaluate various geological, topographical, climatic, vegetation, and anthropogenic factors to determine the extent of landslide likelihood in a specific region [2]. The calculation methods for landslide susceptibility can be primarily divided into knowledge-driven approaches and data-driven approaches [3, 4]. Knowledge-driven methods can make use of the experiences of the experts [5]. Knowledge-driven assessment methods focus on utilizing experts' subjective judgments and domain knowledge to explain the mechanisms of landslide occurrence and apply them in the assessment of landslidesusceptibility. However, it is subjective, and the opinions of experts may be incorrect. Data-driven models are primarily based on the statistical analysis theory [6]. Data-driven methods refer to the utilization of large amounts of observational data and data analysis techniques such as machine learning. Through processing and analyzing the data, patterns and characteristics of landslide occurrence are directly extracted from the data to predict and evaluate the likelihood and hazard of landslide occurrence. Compared to traditional assessment methods based on expert knowledge and experience, data-driven methods place greater emphasis on obtaining information from actual data and can adapt to more complex geological environments and climatic conditions [7]. Commonly used data-driven methods include logistic regression (LR) and decision trees (DTs), which have been widely used in early assessments, along with kernel density estimation (KDE), which identifies high-risk areas based on historical data [8]. As computing technology has evolved, data-driven methods have advanced into more sophisticated modern machine learning techniques, such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs) [9; 10]. These methods have significantly improved LSA in regions like the Himalayas, Iran, and China, offering more accurate predictions and better hazard management solutions [11; 12; 13; 14; 15; 16; 17]. Sample selection in landslide susceptibility research is crucial. [PERSON] et al. [18] used a non-supervised classification method to cluster landslide susceptibility areas and select non-landslide grid cells from extremely low-susceptibility zones. [PERSON] et al. [7] employed fuzzy c-means clustering to improve the quality of \"non-landslide\" samples in the LSA model. Meanwhile, [PERSON] et al. [19] addressed the issue of unbalanced positive and negative samples that can arise from global random negative sampling strategies by utilizing graph neural networks. In terms of model training, various methods have been proposed. [PERSON] et al. [20] and [PERSON] et al. [21] introduced a multi-kernel SVM approach that considers optimized sample selection for LSA. [PERSON] et al. investigated the application of convolutional neural networks (CNNs) in landslide susceptibility mapping in Yanshan County, China [22]. [PERSON] et al. compared conventional recurrent neural networks with three of their variants, long short-term memory, gated recurrent unit, and simple recurrent unit, in a landslide susceptibility case study [23; 24]. [PERSON] et al.