Combining machine learning (ML) and participatory rural appraisal (PRA) for disaster risk preparedness (DRP): Evidence from the poorest region of Luzon, Philippines

In the field of social science, disaster risk preparedness (DRP) is considered immeasurable due to its multidimensional nature, making it infamously difficult to quantify. The current measurements are costly, labor-intensive, and time-consuming. Consequently, policymakers struggle to target policies effectively when implementing disaster risk reduction management initiatives. By combining Participatory Rural Appraisal (PRA) and Machine Learning (ML) to train and test community-based system datasets, this work proposes novel approaches to DRP in the poorest region of Luzon, Philippines. We utilized sophisticated econometrics models along with ML categorization methods. Through the analysis of 34 locales and 4 sectors within a disaggregation system over 429 ensemble runs using cross-validation techniques, we then combined the results. The Support Vector Machine (SVM) classifier achieved the highest accuracy of 91.55 % randomly and 94.53 % within the pipeline, surpassing all other models. It also confirms the current relationship between DRP and multidimensional attributes (a total of 21 factors) in terms of correlation and causation. Our work showcases the potential of ML for disaster risk prediction, potentially reducing costs, saving labor, and optimizing time, especially in the most impoverished areas of the Philippines. Ultimately, through extensive PRA, the outcomes have provided different localities with tools for targeting policies in disaster risk management.