The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Oystein B Dick - One of the best experts on this subject based on the ideXlab platform.
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spatial Prediction of landslide hazards in hoa binh province vietnam a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models
Catena, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial Prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The Prediction-rate curves and area under the curves were calculated to assess Prediction Capability. The results show that all the models have good Prediction capabilities. The model derived using evidential belief functions has the highest Prediction Capability. The model derived using fuzzy SUM has the lowest Prediction Capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same Prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes.
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landslide susceptibility mapping at hoa binh province vietnam using an adaptive neuro fuzzy inference system and gis
Computers & Geosciences, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the Prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the Prediction Capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest Prediction Capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.
Inge Revhaug - One of the best experts on this subject based on the ideXlab platform.
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a comparative assessment of decision trees algorithms for flash flood susceptibility modeling at haraz watershed northern iran
Science of The Total Environment, 2018Co-Authors: Khaba Khosravi, Inh Thai Pham, Kamra Chapi, Ataollah Shirzadi, Hima Shahabi, Inge Revhaug, Indra PrakashAbstract:Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naive Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the Prediction Capability of the models. Results show that the ADT model has the highest Prediction Capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.
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spatial Prediction of landslide hazards in hoa binh province vietnam a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models
Catena, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial Prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The Prediction-rate curves and area under the curves were calculated to assess Prediction Capability. The results show that all the models have good Prediction capabilities. The model derived using evidential belief functions has the highest Prediction Capability. The model derived using fuzzy SUM has the lowest Prediction Capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same Prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes.
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landslide susceptibility mapping at hoa binh province vietnam using an adaptive neuro fuzzy inference system and gis
Computers & Geosciences, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the Prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the Prediction Capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest Prediction Capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.
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Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
Mathematical Problems in Engineering, 2012Co-Authors: Dieu Tien Bui, Biswajeet Pradhan, Owe Löfman, Inge RevhaugAbstract:The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naive Bayes (NB) models for spatial Prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest Prediction Capability. The model derived using DT has the lowest Prediction Capability. Compared to the logistic regression model, the Prediction Capability of the SVM models is slightly better. The Prediction Capability of the DT and NB models is lower.
Biswajeet Pradhan - One of the best experts on this subject based on the ideXlab platform.
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spatial Prediction of landslide hazards in hoa binh province vietnam a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models
Catena, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The main objective of this study is to evaluate and compare the results of evidential belief functions and fuzzy logic models for spatial Prediction of landslide hazards in the Hoa Binh province of Vietnam, using geographic information systems. First, a landslide inventory map showing the locations of 118 landslides that have occurred during the last ten years was constructed using data from various sources. Then, the landslide inventory was randomly partitioned into training and validation datasets (70% of the known landslide locations were used for training and building the landslide models and the remaining 30% for the model validation). Secondly, nine landslide conditioning factors were selected (i.e., slope, aspect, relief amplitude, lithology, landuse, soil type, distance to roads, distance to rivers and distance to faults). Using these factors, landslide susceptibility index values were calculated using evidential belief functions and fuzzy logic models. Finally, landslide susceptibility maps were validated and compared using the validation dataset that was not used in the model building. The Prediction-rate curves and area under the curves were calculated to assess Prediction Capability. The results show that all the models have good Prediction capabilities. The model derived using evidential belief functions has the highest Prediction Capability. The model derived using fuzzy SUM has the lowest Prediction Capability. The fuzzy PRODUCT and fuzzy GAMMA models have almost the same Prediction capabilities. In general, all the models yield reasonable results that may be used for preliminary landuse planning purposes.
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landslide susceptibility mapping at hoa binh province vietnam using an adaptive neuro fuzzy inference system and gis
Computers & Geosciences, 2012Co-Authors: Biswajeet Pradhan, Inge Revhaug, Owe Löfman, Oystein B DickAbstract:The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the Prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the Prediction Capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest Prediction Capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.
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Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
Mathematical Problems in Engineering, 2012Co-Authors: Dieu Tien Bui, Biswajeet Pradhan, Owe Löfman, Inge RevhaugAbstract:The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naive Bayes (NB) models for spatial Prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest Prediction Capability. The model derived using DT has the lowest Prediction Capability. Compared to the logistic regression model, the Prediction Capability of the SVM models is slightly better. The Prediction Capability of the DT and NB models is lower.
Rémy Willinger - One of the best experts on this subject based on the ideXlab platform.
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Head injury Prediction Capability of the HIC, HIP, SIMon and ULP criteria.
Accident; analysis and prevention, 2008Co-Authors: Daniel Marjoux, Daniel Baumgartner, Caroline Deck, Rémy WillingerAbstract:The objective of the present study is to synthesize and investigate using the same set of sixty-one real-world accidents the human head injury Prediction Capability of the head injury criterion (HIC) and the head impact power (HIP) based criterion as well as the injury mechanisms related criteria provided by the simulated injury monitor (SIMon) and the Louis Pasteur University (ULP) finite element head models. Each accident has been classified according to whether neurological injuries, subdural haematoma and skull fractures were reported. Furthermore, the accidents were reconstructed experimentally or numerically in order to provide loading conditions such as acceleration fields of the head or initial head impact conditions. Finally, thanks to this large statistical population of head trauma cases, injury risk curves were computed and the corresponding regression quality estimators permitted to check the correlation of the injury criteria with the injury occurrences. As different kinds of accidents were used, i.e. footballer, motorcyclist and pedestrian cases, the case-independency could also be checked. As a result, FE head modeling provides essential information on the intracranial mechanical behavior and, therefore, better injury criteria can be computed. It is clearly shown that moderate and severe neurological injuries can only be distinguished with a criterion that is computed using intracranial variables and not with the sole global head acceleration.
Dieu Tien Bui - One of the best experts on this subject based on the ideXlab platform.
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Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
ISPRS International Journal of Geo-Information, 2018Co-Authors: Abdelaziz Merghadi, Boumezbeur Abderrahmane, Dieu Tien BuiAbstract:Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide Prediction Capability is highly important. The main objective of this study is to assess and compare the Prediction Capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The Prediction Capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest Prediction Capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment.
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Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of Prediction Capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
Theoretical and Applied Climatology, 2015Co-Authors: Binh Thai Pham, Dieu Tien Bui, Hamid Reza Pourghasemi, Prakash Indra, M. B. DholakiaAbstract:The objective of this study is to make a comparison of the Prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naive Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the Prediction Capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive Capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive Capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive Capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.
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Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
Mathematical Problems in Engineering, 2012Co-Authors: Dieu Tien Bui, Biswajeet Pradhan, Owe Löfman, Inge RevhaugAbstract:The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naive Bayes (NB) models for spatial Prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest Prediction Capability. The model derived using DT has the lowest Prediction Capability. Compared to the logistic regression model, the Prediction Capability of the SVM models is slightly better. The Prediction Capability of the DT and NB models is lower.
