## 1 Introduction
In addition, correlations between soil grain size distribution and engineering behavior have been established, marking an important development in the field of geotechnical engineering. In situ and laboratory testing continue to be the preferred methods for determining construction project design parameters. In the early stages of a project, when site investigation data may not yet be available, correlations based on historical laboratory or field data are useful [21; 22] for estimating the subsurface condition of a site. When determining liquefaction susceptibility, the cyclic resistance ratio (CRR) and cyclic stress ratio (CSR) are vital parameters. The CRR reflects the soil's resistance to liquefaction, as determined by laboratory analysis of soil samples. CSR is dependent on seismic loading conditions and soil characteristics at a particular site [20; 23; 24; 25; 26; 27]. However, developing correlations has been difficult due to the scarcity of dependable and exhaustive data sets. To address this challenge, researchers have integrated empirical data and developed calibrated models to improve the accuracy of predictions and increase knowledge of soil behavior. These advancements in soil testing and correlations have substantially advanced the field of geotechnical engineering. Geotechnical engineering has seen significant advancements in modeling techniques--from simple analytical methods to complex numerical modeling techniques. Initially, mathematical equations were used to predict soil behavior, but recent research has shown that machine learning is a reliable method for predicting geotechnical parameters, [28; 29; 30], particularly liquefaction [31; 32; 33]. Numerous investigations have employed machine learning algorithms to forecast geotechnical characteristics, such as the potency of stabilized soils [28]. Some of these investigations have employed several models concurrently to assess their efficacy [34]. Empirical evidence suggests that the precision of these models is significantly influenced by the volume of data utilized, as a larger dataset leads to more precise predictions [35]. Geostatistics, a field of study that encompasses techniques such as kriging and inverse weighted triangle, has conventionally been employed to interpolate geotechnical characteristics and generate digital maps [36]. However, geostatistics has limitations when it comes to estimating the distribution of numerical data, which has led to alternative techniques such as machine learning being explored. The integration of machine learning and geostatistics has the potential to facilitate the estimation of geotechnical properties in regions with limited data availability. Moreover, this approach can be effectively employed to estimate geotechnical properties that exhibit high anisotropy [36; 37]. The emergence of digital technology has significantly transformed data collection in the field of geotechnical engineering. Geotechnical engineers are now able to employ geospatial intelligence as a promising tool to analyze and visualize geographic information of sites that are susceptible to liquefaction, thanks to the advent of various tools. This technology allows the processing of geotechnical data and reports without physically visiting the location, making it more efficient and effective in planning projects [38]. In geospatial intelligence, surficial studies were commonly conducted since data were easily obtainable, examples are deformations of cultural heritage structures [39], maritime safety [40], flood risk [41], urban land use [42], land subsidence [43], rainfall-runoff relationship [44], and forest fire spread [45]. The utilization of geospatial intelligence in the analysis of liquefaction phenomena has received comparatively little attention, with only a limited number of investigations having been conducted to date. The present study endeavors to fill the gap by utilizing machine learning algorithms to estimate the vulnerability of liquefaction in a case study conducted in Metro Manila, Philippines.