# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### First Phase: Data Collection and Preprocessing  
An accurate and reliable landslide inventory map is essential for landslide susceptibility evaluations [25; 26; 27]. In most cases, its construction relies on geological survey reports, remote sensing imagery, and field surveys, providing a comprehensive record of past landslides. Beyond identifying the locations and types of landslides, the inventory map also serves as a valuable tool for predicting future landslide occurrences, aiding in risk assessment and mitigation efforts [28; 29; 30]. In the present study, 87 landslides were identified based on the landslide inventory map of the study area. Since the landslides that have been identified in the study area have different shapes and sizes, the centroid of each landslide polygon was used for the landslide susceptibility assessments. To create a balanced landslide database, the same number of non-landslide points were randomly selected from the areas that were free of landslides within the study area. The next step included the division of the original dataset into training and testing datasets, using a ratio of 70:30. A total of 16 landslide conditioning factors were selected for the landslide susceptibility assessment in the study area: elevation, NDVI, rainfall, soil type, land use, lithology, slope, aspect, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), plan curvature, profile curvature, and distances to faults, roads, and rivers (Table 1). The selection of these factors was guided by the geological and environmental characteristics of the study area, combined with insights from previous research to ensure a comprehensive assessment. Moreover, in the case of numerical conditioning factors, we applied the Natural Breaks (Jenks) classification method to ensure that the grouping reflected local variability and data structure [31]. The Normalized Difference Vegetation Index (NDVI) reflects the vegetation coverage, with higher values indicating denser vegetation, which can influence the landslide susceptibility [32]. Vegetation plays a crucial role in slope stability by reinforcing the soil and reducing surface erosion, while areas with low NDVI values (indicating sparse or absent vegetation) are generally more prone to landslides. The NDVI map (Figure 3a), derived from Landsat 8 Operational Land Imager data, helps identify regions where reduced vegetation cover may contribute to an increased likelihood of landslide occurrences. Landslides are strongly influenced by the rainfall intensity and total precipitation, although their impact is highly dependent on the topography [33]. Steep slopes and the soil composition  
Figure 2: Flowchart of the following methodology study area. affect how rainfall infiltrates or accumulates, potentially triggering slope failures. The rainfall map (Figure 3b) was created using annual average precipitation data, with the highest recorded rainfall reaching 563.85 mm/year, providing insight into regions where excessive rainfall may contribute to increased landslide susceptibility. Different soil types vary in permeability, directly influencing the landslide susceptibility, as soils with low permeability can lead to water accumulation and slope instability, while more permeable soils achieve better drainage, reducing the likelihood of failure. The soil map (Figure 3c) was compiled from a 1:1,000,000-scale soil type map, providing essential information for assessing the role of soil characteristics in landslide occurrence. Land use is closely linked to human activities, which can either increase or decrease landslide susceptibility. Urbanization, deforestation, and agricultural practices can destabilize slopes, while vegetation-covered areas may help reinforce the soil and prevent erosion. The land use map (Figure 3d) was extracted from a 1:100,000-scale land use plan, providing insight into how different land use types influence the slope stability and landslide risk.