Influence of the Spatio-Temporal Dynamics of Land Use and Land Cover on Flood Risk / Yacine Hasnaoui (2026)

Public ISBD 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 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 Langues : Français (fre) Mots-clés : Flash floods, GeoAI, Machine Learning, Hydrodynamic Modeling, GIS. 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 . - 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 Langues : Français (fre) Mots-clés : Flash floods, GeoAI, Machine Learning, Hydrodynamic Modeling, GIS. 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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Mapping flood susceptibility areas and assessing influential factors / Abdessamed Chagroune (2023) 

Public ISBD 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.

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CHAGROUNE.Abdessamed_HALFAOUI.Mustapha.pdf URL