Household evacuation preparation time during a cyclone: Random Forest algorithm and variable degree analysis

Abstract

Household evacuation preparation time is important to ensure safe and successful evacuations and is essential for the estimation of the total evacuation time during a disaster. Previous research has shown that machine learning can provide a higher prediction accuracy, especially using the random forest model. However, no studies have investigated predictions of household evacuation preparation time considering the safe evacuation of coastal communities during cyclone disasters. This study proposes a methodology to predict household evacuation preparation time following demographic and behavioral input variables based on a random forest algorithm focusing on cyclones. In addition, this research analyzes the variable importance and partial dependence plot to identify the key influential factors that affect household evacuation preparation time. A case study was conducted in Gabura Union, Shaymnagar Upzila in Bangladesh regarding cyclone Bulbul in 2019 to gather demographic and behavioral data for a preparation time simulation. The prediction results showed efficient assessment of household evacuation preparation time prediction, meriting application for cases of future disasters. Our results show that the most important factors that impact household evacuation preparation time are evacuation companions and age, followed by shelter distance, income, and shelter type. The results of the prediction model can assist emergency response and evacuation planners and national disaster management authorities in developing and improving effective evacuation plans that take household evacuation preparation time into consideration for future disasters.