Predicting priority needs for rehabilitation of refugees based on machine learning techniques from monitoring data of rohingya refugees in Bangladesh

Abstract

Ethnic cleansing of Rohingya ethnicity from the Rakhine state of Myanmar has made life miserable for more than half million persons who had fled away with their life from their own country. They have taken shelter and and have been living in in the resource-poor side of Bangladesh. Immense size of refugee population makes it challenging to accommodate all the needs. In case of refugee rehabilitation, all the refugees are given shelter in small camps. Different camps have different types of people and needs. However, not all the needs can be met altogether. So, prioritizing needs will make the rehabilitation process more effective. In this paper, we have used machine learning techniques to identify an effective model which predicts the needs based on priority. This learned model can be used to predict the prioritized needs for different camps while rehabilitation process goes on. Our experiments disclosed that Random Forest ensemble methods work effectively.