## 4 Conclusions  
In conclusion, the successful application of machine learning algorithms to estimate liquefaction susceptibility in the Metro Manila, Philippines study demonstrates the significance of incorporating geotechnical factors and sufficient data for accurate modeling. The study's findings indicate that an increase in usable borehole data leads to improved model performance, as evidenced by the decrease in RMSE and the increase in R\({}^{2}\). These results highlight the importance of data in improving the accuracy of machine learning models and the potential of these algorithms to inform liquefaction risk assessments and mitigation efforts beyond the study area. In addition, this research has demonstrated the practicality of using latitude and longitude as input variables to estimate site-specific characteristics such as ground elevation, groundwater elevation, soil type, and various soil properties. The highly positive correlation between the trained models for ground elevation, groundwater table elevation, SPT N-value, and fines content indicates a good fit with the data. Moreover, among competing models, the nearest neighbor model has demonstrated the highest level of accuracy in estimating the soil type for each layer. Nonetheless, the study also uncovered a significant limitation in site-specific models. Due to the large number of variables, including spatial relationships, it is impossible to represent them with a single equation. Thus, these site-specific trained models are commonly known as black boxes. This limitation highlights the need for additional research to develop more interpretable models that can shed light on the mechanisms underlying the observed patterns. In addition, a calibrated model represented by an equation was utilized in this study to determine various soil properties, such as liquefaction susceptibility and maximum surface ground acceleration (PGA). The linear model has demonstrated a highly significant positive correlation for PGA, and the estimated values fall within a reasonable range when compared to the findings of other studies. Nevertheless, the formula for estimating PGA isrestricted to locations within a 100 km radius of seismic sources, even though the distance can exceed 100 km. In addition, the comprehensive study on seismic activity in Metropolitan Manila, particularly on the Marikina West Valley Fault, has revealed alarming findings that pose an important danger to the safety and security of the area's residents. The conducted parametric analysis to determine the development of peak ground acceleration for earthquakes ranging in magnitude from 5.0 to 9.0 resulted in a map of Metro Manila's estimated PGA that is consistent with previous research, thereby bolstering the validity of the study's findings. In addition, the study's calculations of probability of liquefaction (PL) and liquefaction severity index (LSI) for each layer, as well as the resulting liquefaction potential map, indicate that the coastal and plateau cities of Metro Manila have the highest liquefaction potential. This emphasizes the need for immediate action to mitigate the threat and safeguard the lives of area residents. The validation process utilizing unused Dampalit, Malabon City data further demonstrates the high degree of accuracy of the model developed in the study, thereby enhancing confidence in the reliability of the model. Conceptualization, J.D. and J.G. ; methodology, [PERSON] and J.G. ; software, [PERSON]; validation, J.D. and J.G. ; formal analysis, [PERSON] and J.G. ; investigation, [PERSON] and J.G. ; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript. This research was funded by the Department of Science and Technology Grants-in-Aid (DOST-GIA) GEMMMS Project Fund No. 2021-017. Not applicable. Thank you to DLSU, GEMMMS Project, and DOST.