A near real time flood hazard assessment tool using Satellite Data and Artificial Intelligence

Abstract

This work aims to develop a constantly updated flood hazard assessment tool that utilizes readily available datasets. It is based on the recently released global land use/land cover (LULC) dataset Dynamic World, which is readily available as an open data source within Google Earth Engine platform. It is updated constantly following the release rate of Senitnel-2 images, i.e., every 2 to 5 days depending on the location. The tool presented herein identifies the number of times each 10 m pixel is characterized as flooded for a selected time period, and it is updated accordingly with the new flood events, thus providing a near real time detection of flooded areas. The detected areas are compared against the Regional Flood Hazard Maps developed in the frames of the EU Water Framework Directive, and several inconsistencies of the two approaches were highlighted and commented on. Besides, a new variable is proposed, named Maximum Potential Economic Loss is introduced in order to assess the socioeconomic implications. The presented methodology demonstrates how remotely sensed based datasets, becoming available at increasing rates, can be used as an operational instrument in order to define and update the flood hazard zones required for water resources management plans and policy makers. Our results indicated the usefulness of the complementary use of the conventional flood hazard mapping approaches with those based on emerging techniques and datasets.