1139 | 119 | Flood-prone urban area mapping using machine learning, a case sudy of M’sila city (Algeria)

This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M’sila, which is among the areas most vulnerable to floods._x000D_
This study drew a map of flood-prone areas based on the methodology where we have done a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbor algorithm. Each of them gave an accuracy respectively of 97.92 – 95 – 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost)._x000D_

Dr MEDJADJ Tarek
University of M’sila

ID Abstract: 119