Sand occupies a great proportion of the cementitious matrix product and in particular mortars. He... more Sand occupies a great proportion of the cementitious matrix product and in particular mortars. Hence, the study of fine aggregates used for concrete and mortar in general, deserves to be objects of research including sand which has always been considered as inert material, whose role is exclusively physical. The study of mortars performances based on prepared sand to assess the effect of the type and rate of substitution of mineral additions pozzolana and blast furnace slag of a natural sand fine fraction (sieve diameter less than 0.16 mm) is seen as the main objective of the present experimental research work. The natural sand replaced size is less than 160 μm and for rheological reasons the maximum replacement rate of natural sand is limited to 10%. The results obtained show a significant improvement of the mechanical properties for the mortars based on the new activated sand. With regard to durability tests of HCl and H2SO4 acids chemical attacks, the substitution of the quartz b...
Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions, 2018
GIS-based approaches are often used for Geo-hazards environmental impact assessment (Mezaal et al... more GIS-based approaches are often used for Geo-hazards environmental impact assessment (Mezaal et al. 2017). It allows the integration of different data layers in complex environments (Pradhan et al. 2011). In this research the zoning of the spatial occurrence of a slope movement (SM) could help effectively to reduce their damages (Hadji et al. 2017).
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous... more Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate = 80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.
The aim of this study is to develop landslide susceptibility models for the northern part of the ... more The aim of this study is to develop landslide susceptibility models for the northern part of the Bordj Bou Arreridj (BBA) region, Northeast Algeria, to reduce the physical degradation caused by landslides and, to inspect what is required to properly control it. A comprehensive landslide inventory and susceptibility assessment of this region are not available, even though this region is prone to frequent disruption by geological hazards, mainly landslides. To achieve this objective, an inventory map and 12 variables (including geomorphic, geological, hydrological and environmental factors) are created. The inventory dataset is divided to training dataset with 148 landslides (70%) and validation dataset with 64 landslides (30%). Then, 2 machine learning (ML) techniques are applied to learn the internal relationship between the target set (212 landslide locations) and the 12 variables as inputs. The used methods are Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Their perf...
Abstract Landslides are abundant in mountainous regions. They are responsible for substantial dam... more Abstract Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC = 97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
Sand occupies a great proportion of the cementitious matrix product and in particular mortars. He... more Sand occupies a great proportion of the cementitious matrix product and in particular mortars. Hence, the study of fine aggregates used for concrete and mortar in general, deserves to be objects of research including sand which has always been considered as inert material, whose role is exclusively physical. The study of mortars performances based on prepared sand to assess the effect of the type and rate of substitution of mineral additions pozzolana and blast furnace slag of a natural sand fine fraction (sieve diameter less than 0.16 mm) is seen as the main objective of the present experimental research work. The natural sand replaced size is less than 160 µm and for rheological reasons the maximum replacement rate of natural sand is limited to 10%. The results obtained show a significant improvement of the mechanical properties for the mortars based on the new activated sand. With regard to durability tests of HCl and H 2 SO 4 acids chemical attacks, the substitution of the quartz by active mineral additions in the sand-size skeleton allows an advantageous reduction in loss of resistance up to 50% and a mass gain around 75%.
This research work deals with the landslide susceptibility assessment using Analytic hierarchy pr... more This research work deals with the landslide susceptibility assessment using Analytic hierarchy process (AHP) and information value (IV) methods along a highway road section in Constantine region, NE Algeria. The landslide inventory map which has a total of 29 single landslide locations was created based on historical information, aerial photo interpretation, remote sensing images, and extensive field surveys. The different landslide influencing geoenvironmental factors considered for this study are lithology, slope gradient, slope aspect, distance from faults, land use, distance from streams, and geotechnical parameters. A thematic layer map is generated for every geoenvironmental factor using Geographic Information System (GIS); the lithological units and the distance from faults maps were extracted from the geological database of the region. The slope gradient, slope aspect, and distance from streams were calculated from the Digital Elevation Model (DEM). Contemporary land use map was derived from satellite images and field study. Concerning the geotechnical parameters maps, they were determined making use of the geotechnical data from laboratory tests. The analysis of the relationships between the landsliderelated factors and the landslide events was then carried out in GIS environment. The AUC plot showed that the susceptibility maps had a success rate of 77 and 66% for IV and AHP models, respectively. For that purpose, the IV model is better in predicting the occurrence of landslides than AHP one. Therefore, the information value method could be used as a landslide susceptibility mapping zonation method along other sections of the A1 highway.
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous... more Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate = 80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.
Landslides are abundant in mountainous regions. They are responsible for substantial damages and ... more Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC ¼ 97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
Landslides are dangerous events which threaten both human life and property. The study aims to an... more Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost ARTICLE HISTORY
Sand occupies a great proportion of the cementitious matrix product and in particular mortars. He... more Sand occupies a great proportion of the cementitious matrix product and in particular mortars. Hence, the study of fine aggregates used for concrete and mortar in general, deserves to be objects of research including sand which has always been considered as inert material, whose role is exclusively physical. The study of mortars performances based on prepared sand to assess the effect of the type and rate of substitution of mineral additions pozzolana and blast furnace slag of a natural sand fine fraction (sieve diameter less than 0.16 mm) is seen as the main objective of the present experimental research work. The natural sand replaced size is less than 160 μm and for rheological reasons the maximum replacement rate of natural sand is limited to 10%. The results obtained show a significant improvement of the mechanical properties for the mortars based on the new activated sand. With regard to durability tests of HCl and H2SO4 acids chemical attacks, the substitution of the quartz b...
Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions, 2018
GIS-based approaches are often used for Geo-hazards environmental impact assessment (Mezaal et al... more GIS-based approaches are often used for Geo-hazards environmental impact assessment (Mezaal et al. 2017). It allows the integration of different data layers in complex environments (Pradhan et al. 2011). In this research the zoning of the spatial occurrence of a slope movement (SM) could help effectively to reduce their damages (Hadji et al. 2017).
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous... more Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate = 80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.
The aim of this study is to develop landslide susceptibility models for the northern part of the ... more The aim of this study is to develop landslide susceptibility models for the northern part of the Bordj Bou Arreridj (BBA) region, Northeast Algeria, to reduce the physical degradation caused by landslides and, to inspect what is required to properly control it. A comprehensive landslide inventory and susceptibility assessment of this region are not available, even though this region is prone to frequent disruption by geological hazards, mainly landslides. To achieve this objective, an inventory map and 12 variables (including geomorphic, geological, hydrological and environmental factors) are created. The inventory dataset is divided to training dataset with 148 landslides (70%) and validation dataset with 64 landslides (30%). Then, 2 machine learning (ML) techniques are applied to learn the internal relationship between the target set (212 landslide locations) and the 12 variables as inputs. The used methods are Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Their perf...
Abstract Landslides are abundant in mountainous regions. They are responsible for substantial dam... more Abstract Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC = 97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
Sand occupies a great proportion of the cementitious matrix product and in particular mortars. He... more Sand occupies a great proportion of the cementitious matrix product and in particular mortars. Hence, the study of fine aggregates used for concrete and mortar in general, deserves to be objects of research including sand which has always been considered as inert material, whose role is exclusively physical. The study of mortars performances based on prepared sand to assess the effect of the type and rate of substitution of mineral additions pozzolana and blast furnace slag of a natural sand fine fraction (sieve diameter less than 0.16 mm) is seen as the main objective of the present experimental research work. The natural sand replaced size is less than 160 µm and for rheological reasons the maximum replacement rate of natural sand is limited to 10%. The results obtained show a significant improvement of the mechanical properties for the mortars based on the new activated sand. With regard to durability tests of HCl and H 2 SO 4 acids chemical attacks, the substitution of the quartz by active mineral additions in the sand-size skeleton allows an advantageous reduction in loss of resistance up to 50% and a mass gain around 75%.
This research work deals with the landslide susceptibility assessment using Analytic hierarchy pr... more This research work deals with the landslide susceptibility assessment using Analytic hierarchy process (AHP) and information value (IV) methods along a highway road section in Constantine region, NE Algeria. The landslide inventory map which has a total of 29 single landslide locations was created based on historical information, aerial photo interpretation, remote sensing images, and extensive field surveys. The different landslide influencing geoenvironmental factors considered for this study are lithology, slope gradient, slope aspect, distance from faults, land use, distance from streams, and geotechnical parameters. A thematic layer map is generated for every geoenvironmental factor using Geographic Information System (GIS); the lithological units and the distance from faults maps were extracted from the geological database of the region. The slope gradient, slope aspect, and distance from streams were calculated from the Digital Elevation Model (DEM). Contemporary land use map was derived from satellite images and field study. Concerning the geotechnical parameters maps, they were determined making use of the geotechnical data from laboratory tests. The analysis of the relationships between the landsliderelated factors and the landslide events was then carried out in GIS environment. The AUC plot showed that the susceptibility maps had a success rate of 77 and 66% for IV and AHP models, respectively. For that purpose, the IV model is better in predicting the occurrence of landslides than AHP one. Therefore, the information value method could be used as a landslide susceptibility mapping zonation method along other sections of the A1 highway.
Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous... more Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate = 80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.
Landslides are abundant in mountainous regions. They are responsible for substantial damages and ... more Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC ¼ 97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.
Landslides are dangerous events which threaten both human life and property. The study aims to an... more Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost ARTICLE HISTORY
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