Fatiha Debiche
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Fatiha Debiche has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Alexandru-Ionut Petrisor
Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, Academiei 18-20, 010014 Bucharest, Romania
2 shared publications - Mohammed Amin Benbouras
Built Environment Research Laboratory (LBE), Department of Structure and Materials, University of Science and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria
2 shared publications - Abdelghani Leghouchi
Civil Engineering Department, Mohammed Seddik Benyahia University, Jijel 18000, Algeria
1 shared publication - Lyes Mohamed Baba Ali
Faculty of Earth Sciences, Geography and Territorial Planning, University of Science and Technology Houari Boumediene, Algiers 16024, Algeria
1 shared publication - Mohamed Lyes Baba Ali
Department of Physics and Geology, Piazza Università, 06123 Perugia, Italy
1 shared publication - Salima Ait El Hocine
Laboratory of Environment, Water, Geomechanics and Structures (LEEGO), University of Science and Technology Houari Boumediene, Algiers 16111, Algeria
1 shared publication
Latest Publications
- Open Access
GeoLiquefy-AI: Predicting Soil Liquefaction Potential via Deep Neural Architecture Search in Seismically Active Coastal Zones
Earthquake-induced soil liquefaction represents a severe geohazard causing catastrophic infrastructure failure in prone coastal zones, requiring an advanced environmental spatial assessment for their sustainable land-use planning. This study utilizes advanced computational intelligence models to predict earthquake-induced soil liquefaction in Boumerdès, Algeria, an area heavily affected by the 2003 (Mw 6.8) earthquake. Utilizing a comprehensive subsurface database of 1984 geotechnical records encompassing lithology, hydrogeological configurations, and seismic parameters, advanced deep learning frameworks are developed and optimized via automated Neural Architecture Search (NAS). The continuous Factor of Safety (Fs) is calculated to distinguish stable profiles from vulnerable strata, benchmarking conventional ANN and DNN models against NAS-optimized variants (NAS-ANN and NAS-DNN) using a stratified 5-fold cross-validation scheme. The optimized hybrid NAS-DNN framework effectively captured non-linear soil responses, achieving a training correlation coefficient () of 0.9518, a validation coefficient () of 0.8843, and a cross-validated mean of approximately 0.82, demonstrating improved predictive reliability compared to traditional models. Ultimately, this optimal network is embedded into the ‘GeoLiquefy-AI (v1.0)’ interface. To ensure reliability for safety-critical applications, we integrated a SHAP explainable AI framework, validating the model’s geomechanical logic by mapping physical soil-liquefaction dependencies. This deployment-ready tool enables rapid, transparent hazard calculations, providing a scalable platform for seismic microzonation and proactive urban risk mitigation.Open AccessGeoLiquefy-AI: Predicting Soil Liquefaction Potential via Deep Neural Architecture Search in Seismically Active Coastal Zones
Earthquake-induced soil liquefaction represents a severe geohazard causing catastrophic infrastructure failure in prone coastal zones, requiring an advanced environmental spatial assessment for their sustainable land-use planning. This study utilizes advanced computational intelligence models to predict earthquake-induced soil liquefaction in Boumerdès, Algeria, an area heavily affected by the 2003 (Mw 6.8) earthquake. Utilizing a comprehensive subsurface database of 1984 geotechnical records encompassing lithology, hydrogeological configurations, and seismic parameters, advanced deep learning frameworks are developed and optimized via automated Neural Architecture Search (NAS). The continuous Factor of Safety (Fs) is calculated to distinguish stable profiles from vulnerable strata, benchmarking conventional ANN and DNN models against NAS-optimized variants (NAS-ANN and NAS-DNN) using a stratified 5-fold cross-validation scheme. The optimized hybrid NAS-DNN framework effectively captured non-linear soil responses, achieving a training correlation coefficient () of 0.9518, a validation coefficient () of 0.8843, and a cross-validated mean of approximately 0.82, demonstrating improved predictive reliability compared to traditional models. Ultimately, this optimal network is embedded into the ‘GeoLiquefy-AI (v1.0)’ interface. To ensure reliability for safety-critical applications, we integrated a SHAP explainable AI framework, validating the model’s geomechanical logic by mapping physical soil-liquefaction dependencies. This deployment-ready tool enables rapid, transparent hazard calculations, providing a scalable platform for seismic microzonation and proactive urban risk mitigation. - Open Access
Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach
Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world.
Open AccessAdvancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach
Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world.