Using Random Forest Machine learning to estimate the impact of hydrological drought on the shipping industry

Abstract

Hydrological droughts can have severe impacts on water levels in a river and consequentially also on shipping. Traditionally research on the impact of hydrological drought is done by means of numerical modeling. In this study a machine learning approach was used, to investigate the viability of data driven approaches in drought estimations. It was found that random forest machine learning is a promising tool that can be used to study the impact of hydrological drought.