Lyes Mohamed Baba Ali
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lyes Mohamed Baba Ali has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Abdelghani Leghouchi
Civil Engineering Department, Mohammed Seddik Benyahia University, Jijel 18000, Algeria
1 shared publication - Alexandru-Ionut Petrisor
Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, Academiei 18-20, 010014 Bucharest, Romania
1 shared publication - Fatiha Debiche
Built Environment Research Laboratory (LBE), Department of Structure and Materials, University of Science and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria
1 shared publication - Mohammed Amin Benbouras
Built Environment Research Laboratory (LBE), Department of Structure and Materials, University of Science and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria
1 shared publication
Latest Publications
- 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.