| Titre : |
Influence of the Spatio-Temporal Dynamics of Land Use and Land Cover on Flood Risk |
| Titre original : |
Influence de la dynamique spatio-temporelle de l’occupation et de l’utilisation des sols sur le risque d’inondation |
| Type de document : |
document électronique |
| Auteurs : |
Yacine Hasnaoui, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Mundher Zaher Yassen, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2026 |
| Importance : |
1 fichier PDF (5.8 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Thèse de Doctorat :Hydraulique: Alger, Ecole Nationale Polytechnique : 2026
Bibliogr. p. 124-138
Thèse confidentielle 3 ans jusqu'à Décembre 2029 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Flash floods
GeoAI
Machine Learning
Hydrodynamic Modeling
GIS. |
| Index. décimale : |
D00326 |
| Résumé : |
This study addresses the escalating threat of flash floods in Algeria, particularly in the Hodna basin, which is exacerbated by climate change and rapid urbanization. It proposes an innovative
and integrated Geo-AI approach to flood mapping, combining machine learning (ML) techniques with geospatial data and Geographic Information Systems (GIS). The first part focuses on enhancing flash flood prediction, integrating diverse hydrological and topographical factors from multiple data sources. A stacking ensemble methodology was developed,combining CatBoost models with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), and Deep Belief Networks (DBNs). This approach demonstrated exceptional predictive performance, particularly CatBoost CNN,which achieved an accuracy 92% accuracy. The second part analyzes the complex interactions between flood risk and spatio-temporal dynamics of land use and land cover (LULC) changes over a 20 year period (2000-2020) and projects future trends until 2040. Landsat data and a hybrid CA-Markov model were used for LULC classification and future predictions. Complementing these AI-driven predictive approaches, the thesis integrates detailed hydrodynamic simulations using HEC-RAS for critical sections of the Oued El Ksob in M'sila. |
Influence of the Spatio-Temporal Dynamics of Land Use and Land Cover on Flood Risk = Influence de la dynamique spatio-temporelle de l’occupation et de l’utilisation des sols sur le risque d’inondation [document électronique] / Yacine Hasnaoui, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Mundher Zaher Yassen, Directeur de thèse . - [S.l.] : [s.n.], 2026 . - 1 fichier PDF (5.8 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Thèse de Doctorat :Hydraulique: Alger, Ecole Nationale Polytechnique : 2026
Bibliogr. p. 124-138
Thèse confidentielle 3 ans jusqu'à Décembre 2029 Langues : Anglais ( eng)
| Mots-clés : |
Flash floods
GeoAI
Machine Learning
Hydrodynamic Modeling
GIS. |
| Index. décimale : |
D00326 |
| Résumé : |
This study addresses the escalating threat of flash floods in Algeria, particularly in the Hodna basin, which is exacerbated by climate change and rapid urbanization. It proposes an innovative
and integrated Geo-AI approach to flood mapping, combining machine learning (ML) techniques with geospatial data and Geographic Information Systems (GIS). The first part focuses on enhancing flash flood prediction, integrating diverse hydrological and topographical factors from multiple data sources. A stacking ensemble methodology was developed,combining CatBoost models with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), and Deep Belief Networks (DBNs). This approach demonstrated exceptional predictive performance, particularly CatBoost CNN,which achieved an accuracy 92% accuracy. The second part analyzes the complex interactions between flood risk and spatio-temporal dynamics of land use and land cover (LULC) changes over a 20 year period (2000-2020) and projects future trends until 2040. Landsat data and a hybrid CA-Markov model were used for LULC classification and future predictions. Complementing these AI-driven predictive approaches, the thesis integrates detailed hydrodynamic simulations using HEC-RAS for critical sections of the Oued El Ksob in M'sila. |
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D000100 
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