| Titre : |
Mapping flood susceptibility areas and assessing influential factors : case of the Chellif basin |
| Type de document : |
document électronique |
| Auteurs : |
Abdessamed Chagroune, Auteur ; Mustapha Halfaoui, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Yacine Hasnaoui, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2023 |
| Importance : |
1 fichier PDF (11.5 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Hydraulique : Alger, École Nationale Polytechnique : 2023.
Bibliogr. p. 68-73. |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Flood susceptibility
AdaBoost
LGBM
Machine learning
Chellif basin |
| Index. décimale : |
PH00123 |
| Résumé : |
Floods are considered one of the most destructive catastrophic phenomena.Flood susceptibility is defined as the tendency to suffer damage caused by this phenomenon.
However, accurately predicting flash floods remains challenging due to the complexity of the phenomenon. In this study, we adopted an approach based on geographic information systems (GIS), remote sensing techniques (RS), and machine learning classification models such as LGBM, AdaBoost, and the new machine learning technique called Stacking, to create a flood susceptibility map in the Chellif watershed. Fifteen hydrological and topographic factors were used as inputs for the flood susceptibility models. The results showed that Stacking was the most optimal model, with an AUC value of 0.99, followed by LGBM with 0.98 and AdaBoost with 0.96. The findings of this study are used for planning and implementing flood mitigation strategies in the region. |
Mapping flood susceptibility areas and assessing influential factors : case of the Chellif basin [document électronique] / Abdessamed Chagroune, Auteur ; Mustapha Halfaoui, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Yacine Hasnaoui, Directeur de thèse . - [S.l.] : [s.n.], 2023 . - 1 fichier PDF (11.5 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Hydraulique : Alger, École Nationale Polytechnique : 2023.
Bibliogr. p. 68-73. Langues : Anglais ( eng)
| Mots-clés : |
Flood susceptibility
AdaBoost
LGBM
Machine learning
Chellif basin |
| Index. décimale : |
PH00123 |
| Résumé : |
Floods are considered one of the most destructive catastrophic phenomena.Flood susceptibility is defined as the tendency to suffer damage caused by this phenomenon.
However, accurately predicting flash floods remains challenging due to the complexity of the phenomenon. In this study, we adopted an approach based on geographic information systems (GIS), remote sensing techniques (RS), and machine learning classification models such as LGBM, AdaBoost, and the new machine learning technique called Stacking, to create a flood susceptibility map in the Chellif watershed. Fifteen hydrological and topographic factors were used as inputs for the flood susceptibility models. The results showed that Stacking was the most optimal model, with an AUC value of 0.99, followed by LGBM with 0.98 and AdaBoost with 0.96. The findings of this study are used for planning and implementing flood mitigation strategies in the region. |
|
PH00100 
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