The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 115–122, 2022
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-115-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 115–122, 2022
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-115-2022
 
10 Jan 2022
10 Jan 2022

AN ARTIFICIAL INTELLIGENCE APPROACH TO PREDICTION OF EXTREME EVENTS: THE CASE OF STORMS IN WESTERN FRANCE

A. Frifra1,2, M. Maanan1, H. Rhinane1, and M. Maanan2 A. Frifra et al.
  • 1Hassan II University of Casablanca, Geology Department, Faculty of Sciences Ain Chock, 20100 Casablanca, Morocco
  • 2University of Nantes, UMR 6554 CNRS LETG-Nantes, Geography Institute, 44312 Nantes, France

Keywords: Storm prediction, Time series prediction, Deep learning, Machine learning, Gated recurrent unit, Support vector machines, Decision making

Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.