A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh

Abstract

Floods are one of the most frequently occurring disasters in Bangladesh that cause small to large scale damage every year. Most of the studies in the literature provide a flood damage prediction or inference model at individual building level. Some of the works that adopt a higher spatial scale such as households conduct their analysis on a few specific regions. This paper presents a household-level flood damage analysis performed on 2004–2009 flood data from 64 districts of Bangladesh. The study focuses both on prediction and determination of influencing factors because both of these facilitate flood damage reduction programs. A machine learning driven approach has been taken for prediction where three learning algorithms namely linear regression, random forest and artificial neural network are fitted to the data and compared. In this work, linear regression performed better than the other two because its assumptions were considered. A regression analysis showed the significance of the relationship between predictors and damage. Apart from the significant hydrologic predictors, literacy, flood awareness, house structure and disaster management knowledge were found to be influential. Preparedness was observed to be statistically insignificant unless it was combined with disaster management knowledge. A principal component analysis was further performed to cluster different variables into predictor groups and inspect their effect on flood damage. According to this analysis, hydrologic and environmental predictors, literacy, land ownership and house structure were found to be highly important where precaution and disaster related factors showed less significance.