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This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.
Viet-Ha Nhu; Danesh Zandi; Himan Shahabi; Kamran Chapi; Ataollah Shirzadi; Nadhir Al-Ansari; Sushant K. Singh; Jie Dou; Hoang Nguyen. Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran. Applied Sciences 2020, 10, 5047 .
AMA StyleViet-Ha Nhu, Danesh Zandi, Himan Shahabi, Kamran Chapi, Ataollah Shirzadi, Nadhir Al-Ansari, Sushant K. Singh, Jie Dou, Hoang Nguyen. Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran. Applied Sciences. 2020; 10 (15):5047.
Chicago/Turabian StyleViet-Ha Nhu; Danesh Zandi; Himan Shahabi; Kamran Chapi; Ataollah Shirzadi; Nadhir Al-Ansari; Sushant K. Singh; Jie Dou; Hoang Nguyen. 2020. "Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran." Applied Sciences 10, no. 15: 5047.
Water pollution is one of the most important environmental challenges and also one of the main causes of death in the world. Transporting oil products on roads by trucks and their accidents lead to the release of these chemicals into the environment, resulting in water resources pollution. Thus, the main goal of this study is to evaluate the risk assessment of the water resources pollution in the road of Sanandaj to Marivan, Kurdistan province, Iran. Six scenarios for four types of hazardous materials in two seasons of the years were considered. The road was then segmented, and the probability of accidents in each segment was calculated by the Poisson regression method. Two scenarios were selected for analysis since truck accidents had happened mainly in spring (scenario 1) and winter (scenario 4). According to the results, the total risk of water contamination path is 20%very low, 19% low, 29% moderate, 28% high, and 4% very high. Also, according to scenario 1, 14% of the total area of the study area is very low risk, 23% low risk, 15% medium risk, 6% high risk, and 42% are very high risk. Based on scenario 4, 39% of the total area of the study area has a very low risk, 44% low risk, 13% medium risk, 4% high risk. This simply means that this road is not very suitable for transporting hazardous oil products.
Baha Ebrahimi; Salman Ahmadi; Kamran Chapi; Hazhir Amjadi. Risk assessment of water resources pollution from transporting of oil hazardous materials (Sanandaj-Marivan road, Kurdistan Province, Iran). Environmental Science and Pollution Research 2020, 27, 35814 -35827.
AMA StyleBaha Ebrahimi, Salman Ahmadi, Kamran Chapi, Hazhir Amjadi. Risk assessment of water resources pollution from transporting of oil hazardous materials (Sanandaj-Marivan road, Kurdistan Province, Iran). Environmental Science and Pollution Research. 2020; 27 (28):35814-35827.
Chicago/Turabian StyleBaha Ebrahimi; Salman Ahmadi; Kamran Chapi; Hazhir Amjadi. 2020. "Risk assessment of water resources pollution from transporting of oil hazardous materials (Sanandaj-Marivan road, Kurdistan Province, Iran)." Environmental Science and Pollution Research 27, no. 28: 35814-35827.
In recent years, the intensification of drought and unsustainable management and use of water resources have caused a significant decline in the water level of the Urmia Lake in the northwest of Iran. This condition has affected the lake, approaching an irreversible point such that many projects have been implemented and are being implemented to save the natural condition of the Urmia Lake, among which the inter-basin water transfer (IBWT) project from the Zab River to the lake could be considered an important project. The main aim of this research is the evaluation of the IBWT project effects on the Gadar destination basin. Simulations of the geometrical properties of the river, including the bed and flow, have been performed, and the land cover and flood map were overlapped in order to specify the areas prone to flood after implementing the IBWT project. The results showed that with the implementation of this project, the discharge of the Gadar River was approximately tripled and the water level of the river rose 1 m above the average. In April, May, and June, about 952.92, 1458.36, and 731.43 ha of land adjacent to the river (floodplain) will be inundated by flood, respectively. Results also indicated that UNESCO’s criteria No. 3 (“a comprehensive environmental impact assessment must indicate that the project will not substantially degrade the environmental quality within the area of origin or the area of delivery”) and No. 5 (“the net benefits from the transfer must be shared equitably between the area of origin and the area of water delivery”) have been violated by implementing this project in the study area. The findings could help the local government and other decision-makers to better understand the effects of the IBWT projects on the physical and hydrodynamic processes of the Gadar River as a destination basin.
Dieu Tien Bui; Dawood Talebpour Asl; Ezatolla Ghanavati; Nadhir Al-Ansari; Saeed Khezri; Kamran Chapi; Ata Amini; Binh Thai Pham. Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin. Sustainability 2020, 12, 338 .
AMA StyleDieu Tien Bui, Dawood Talebpour Asl, Ezatolla Ghanavati, Nadhir Al-Ansari, Saeed Khezri, Kamran Chapi, Ata Amini, Binh Thai Pham. Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin. Sustainability. 2020; 12 (1):338.
Chicago/Turabian StyleDieu Tien Bui; Dawood Talebpour Asl; Ezatolla Ghanavati; Nadhir Al-Ansari; Saeed Khezri; Kamran Chapi; Ata Amini; Binh Thai Pham. 2020. "Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin." Sustainability 12, no. 1: 338.
This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.
Dieu Tien Bui; Ataollah Shirzadi; Kamran Chapi; Himan Shahabi; Biswajeet Pradhan; Binh Thai Pham; Vijay P. Singh; Wei Chen; Khabat Khosravi; Baharin Bin Ahmad; Saro Lee. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. Water 2019, 11, 2013 .
AMA StyleDieu Tien Bui, Ataollah Shirzadi, Kamran Chapi, Himan Shahabi, Biswajeet Pradhan, Binh Thai Pham, Vijay P. Singh, Wei Chen, Khabat Khosravi, Baharin Bin Ahmad, Saro Lee. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. Water. 2019; 11 (10):2013.
Chicago/Turabian StyleDieu Tien Bui; Ataollah Shirzadi; Kamran Chapi; Himan Shahabi; Biswajeet Pradhan; Binh Thai Pham; Vijay P. Singh; Wei Chen; Khabat Khosravi; Baharin Bin Ahmad; Saro Lee. 2019. "A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping." Water 11, no. 10: 2013.
Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.
Duie Tien Bui; Khabat Khosravi; Himan Shahabi; Prasad Daggupati; Jan F. Adamowski; Assefa M. Melesse; Binh Thai Pham; Hamid Reza Pourghasemi; Mehrnoosh Mahmoudi; Sepideh Bahrami; Biswajeet Pradhan; Ataollah Shirzadi; Kamran Chapi; Saro Lee. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing 2019, 11, 1589 .
AMA StyleDuie Tien Bui, Khabat Khosravi, Himan Shahabi, Prasad Daggupati, Jan F. Adamowski, Assefa M. Melesse, Binh Thai Pham, Hamid Reza Pourghasemi, Mehrnoosh Mahmoudi, Sepideh Bahrami, Biswajeet Pradhan, Ataollah Shirzadi, Kamran Chapi, Saro Lee. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sensing. 2019; 11 (13):1589.
Chicago/Turabian StyleDuie Tien Bui; Khabat Khosravi; Himan Shahabi; Prasad Daggupati; Jan F. Adamowski; Assefa M. Melesse; Binh Thai Pham; Hamid Reza Pourghasemi; Mehrnoosh Mahmoudi; Sepideh Bahrami; Biswajeet Pradhan; Ataollah Shirzadi; Kamran Chapi; Saro Lee. 2019. "Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model." Remote Sensing 11, no. 13: 1589.
In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
Dieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Kamran Chapi; Ebrahim Omidavr; Binh Thai Pham; Dawood Talebpour Asl; Hossein Khaledian; Biswajeet Pradhan; Mahdi Panahi; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors 2019, 19, 2444 .
AMA StyleDieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, Kamran Chapi, Ebrahim Omidavr, Binh Thai Pham, Dawood Talebpour Asl, Hossein Khaledian, Biswajeet Pradhan, Mahdi Panahi, Baharin Bin Ahmad, Hosein Rahmani, Gyula Gróf, Saro Lee. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran). Sensors. 2019; 19 (11):2444.
Chicago/Turabian StyleDieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Kamran Chapi; Ebrahim Omidavr; Binh Thai Pham; Dawood Talebpour Asl; Hossein Khaledian; Biswajeet Pradhan; Mahdi Panahi; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. 2019. "A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)." Sensors 19, no. 11: 2444.
We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.
Dieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing 2019, 11, 931 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, Ataollah Shirzadi, Marten Geertsema, John J. Clague, Khabat Khosravi, Biswajeet Pradhan, Binh Thai Pham, Kamran Chapi, Zahra Barati, Baharin Bin Ahmad, Hosein Rahmani, Gyula Gróf, Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing. 2019; 11 (8):931.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. 2019. "Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm." Remote Sensing 11, no. 8: 931.
Floods around the world are having devastating effect on human lives and properties. In this paper, we tested three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW) along with two machine learning methods (NBT and NB) for their ability to model flood susceptibility in China’s Ningdu Catchment, one of the nation’s most flood-prone areas. The twelve flood conditioning factors used as input parameters were: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The models’ predictive capacity was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model’s performance was the best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising method for the assessment of flood susceptible areas for proper planning and management of flood hazards.
Khabat Khosravi; Himan Shahabi; Binh Thai Pham; Jan Adamowski; Ataollah Shirzadi; Biswajeet Pradhan; Jie Dou; Hai-Bang Ly; Gyula Gróf; Huu Loc Ho; Haoyuan Hong; Kamran Chapi; Indra Prakash. A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology 2019, 573, 311 -323.
AMA StyleKhabat Khosravi, Himan Shahabi, Binh Thai Pham, Jan Adamowski, Ataollah Shirzadi, Biswajeet Pradhan, Jie Dou, Hai-Bang Ly, Gyula Gróf, Huu Loc Ho, Haoyuan Hong, Kamran Chapi, Indra Prakash. A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology. 2019; 573 ():311-323.
Chicago/Turabian StyleKhabat Khosravi; Himan Shahabi; Binh Thai Pham; Jan Adamowski; Ataollah Shirzadi; Biswajeet Pradhan; Jie Dou; Hai-Bang Ly; Gyula Gróf; Huu Loc Ho; Haoyuan Hong; Kamran Chapi; Indra Prakash. 2019. "A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods." Journal of Hydrology 573, no. : 311-323.
Understanding landslide characteristics such as their locations, dimensions, and spatial distribution is of highly importance in landslide modeling and prediction. The main objective of this study was to assess the effect of different sample sizes and raster resolutions in landslide susceptibility modeling and prediction accuracy of shallow landslides. In this regard, the Bijar region of the Kurdistan province (Iran) was selected as a case study. Accordingly, a total of 20 landslide conditioning factors were considered with six different raster resolutions (10 m, 15 m, 20 m, 30 m, 50 m, and 100 m) and four different sample sizes (60/40%, 70/30%, 80/20%, and 90/10%) were investigated. The merit of each conditioning factors was assessed using the Information Gain Ratio (IGR) technique, whereas Alternating decision tree (ADTree), which has been rarely explored for landslide modeling, was used for building models. Performance of the models was assessed using the area under the ROC curve (AUROC), sensitivity, specificity, accuracy, kappa and RMSE criteria. The results show that with increasing the number of training pixels in the modeling process, the accuracy is increased. Findings also indicate that for the sample sizes of 60/40% (AUROC = 0.800) and 70/30% (AUROC = 0.899), the highest prediction accuracy is derived with the raster resolution of 10 m. With the raster resolution of 20 m, the highest prediction accuracy for the sample size of 80/20% (AUROC = 0.871) and 90/10% (AUROC = 0.864). These outcomes provide a guideline for future research enabling researchers to select an optimal data resolution for landslide hazard modeling.
Ataollah Shirzadi; Karim Solaimani; Mahmood Habibnejad Roshan; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Saskia Keesstra; Baharin Bin Ahmad; Dieu Tien Bui. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. CATENA 2019, 178, 172 -188.
AMA StyleAtaollah Shirzadi, Karim Solaimani, Mahmood Habibnejad Roshan, Ataollah Kavian, Kamran Chapi, Himan Shahabi, Saskia Keesstra, Baharin Bin Ahmad, Dieu Tien Bui. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. CATENA. 2019; 178 ():172-188.
Chicago/Turabian StyleAtaollah Shirzadi; Karim Solaimani; Mahmood Habibnejad Roshan; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Saskia Keesstra; Baharin Bin Ahmad; Dieu Tien Bui. 2019. "Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution." CATENA 178, no. : 172-188.
Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
Qingfeng He; Himan Shahabi; Ataollah Shirzadi; Shaojun Li; Wei Chen; Nianqin Wang; Huichan Chai; Huiyuan Bian; Jianquan Ma; Yingtao Chen; Xiaojing Wang; Kamran Chapi; Baharin Bin Ahmad. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Science of The Total Environment 2019, 663, 1 -15.
AMA StyleQingfeng He, Himan Shahabi, Ataollah Shirzadi, Shaojun Li, Wei Chen, Nianqin Wang, Huichan Chai, Huiyuan Bian, Jianquan Ma, Yingtao Chen, Xiaojing Wang, Kamran Chapi, Baharin Bin Ahmad. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Science of The Total Environment. 2019; 663 ():1-15.
Chicago/Turabian StyleQingfeng He; Himan Shahabi; Ataollah Shirzadi; Shaojun Li; Wei Chen; Nianqin Wang; Huichan Chai; Huiyuan Bian; Jianquan Ma; Yingtao Chen; Xiaojing Wang; Kamran Chapi; Baharin Bin Ahmad. 2019. "Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms." Science of The Total Environment 663, no. : 1-15.
Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors and four different statistical approaches: Naïve Bayes (NB), Bayes Net (BN), Logistic Regression (LR), and Bayesian Logistic Regression (BLR). Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio (IGR) technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and ROC‐curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land‐use managers for designing hazard and land‐degradation mitigation plans.
Kamal Taheri; Himan Shahabi; Kamran Chapi; Ataollah Shirzadi; Francisco Gutiérrez; Khabat Khosravi. Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms. Land Degradation & Development 2019, 30, 730 -745.
AMA StyleKamal Taheri, Himan Shahabi, Kamran Chapi, Ataollah Shirzadi, Francisco Gutiérrez, Khabat Khosravi. Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms. Land Degradation & Development. 2019; 30 (7):730-745.
Chicago/Turabian StyleKamal Taheri; Himan Shahabi; Kamran Chapi; Ataollah Shirzadi; Francisco Gutiérrez; Khabat Khosravi. 2019. "Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms." Land Degradation & Development 30, no. 7: 730-745.
The authors wish to make the following corrections to this paper
Dieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Kamran Chapi; Nhat-Duc Hoang; Binh Thai Pham; Quang-Thanh Bui; Chuyen Trung Tran; Mahdi Panahi; Baharin Bin Ahmad; Lee Saro. Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538. Remote Sensing 2018, 11, 57 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Kamran Chapi, Nhat-Duc Hoang, Binh Thai Pham, Quang-Thanh Bui, Chuyen Trung Tran, Mahdi Panahi, Baharin Bin Ahmad, Lee Saro. Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538. Remote Sensing. 2018; 11 (1):57.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Kamran Chapi; Nhat-Duc Hoang; Binh Thai Pham; Quang-Thanh Bui; Chuyen Trung Tran; Mahdi Panahi; Baharin Bin Ahmad; Lee Saro. 2018. "Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538." Remote Sensing 11, no. 1: 57.
Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.
Wei Chen; Himan Shahabi; Shuai Zhang; Khabat Khosravi; Ataollah Shirzadi; Kamran Chapi; Binh Thai Pham; Tingyu Zhang; Huichan Chai; Jianquan Ma; Yingtao Chen; Xiaojing Wang; Renwei Li; Baharin Bin Ahmad. Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression. Applied Sciences 2018, 8, 2540 .
AMA StyleWei Chen, Himan Shahabi, Shuai Zhang, Khabat Khosravi, Ataollah Shirzadi, Kamran Chapi, Binh Thai Pham, Tingyu Zhang, Huichan Chai, Jianquan Ma, Yingtao Chen, Xiaojing Wang, Renwei Li, Baharin Bin Ahmad. Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression. Applied Sciences. 2018; 8 (12):2540.
Chicago/Turabian StyleWei Chen; Himan Shahabi; Shuai Zhang; Khabat Khosravi; Ataollah Shirzadi; Kamran Chapi; Binh Thai Pham; Tingyu Zhang; Huichan Chai; Jianquan Ma; Yingtao Chen; Xiaojing Wang; Renwei Li; Baharin Bin Ahmad. 2018. "Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression." Applied Sciences 8, no. 12: 2540.
The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.
Haoyuan Hong; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Kamran Chapi; Baharin Bin Ahmad; Majid Shadman Roodposhti; Arastoo Yari Hesar; Yingying Tian; Dieu Tien Bui. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural Hazards 2018, 96, 173 -212.
AMA StyleHaoyuan Hong, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Kamran Chapi, Baharin Bin Ahmad, Majid Shadman Roodposhti, Arastoo Yari Hesar, Yingying Tian, Dieu Tien Bui. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural Hazards. 2018; 96 (1):173-212.
Chicago/Turabian StyleHaoyuan Hong; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Kamran Chapi; Baharin Bin Ahmad; Majid Shadman Roodposhti; Arastoo Yari Hesar; Yingying Tian; Dieu Tien Bui. 2018. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods." Natural Hazards 96, no. 1: 173-212.
The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
Ataollah Shirzadi; Karim Soliamani; Mahmood Habibnejhad; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Wei Chen; Khabat Khosravi; Binh Thai Pham; Biswajeet Pradhan; Anuar Ahmad; Baharin Bin Ahmad; Dieu Tien Bui. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors 2018, 18, 3777 .
AMA StyleAtaollah Shirzadi, Karim Soliamani, Mahmood Habibnejhad, Ataollah Kavian, Kamran Chapi, Himan Shahabi, Wei Chen, Khabat Khosravi, Binh Thai Pham, Biswajeet Pradhan, Anuar Ahmad, Baharin Bin Ahmad, Dieu Tien Bui. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors. 2018; 18 (11):3777.
Chicago/Turabian StyleAtaollah Shirzadi; Karim Soliamani; Mahmood Habibnejhad; Ataollah Kavian; Kamran Chapi; Himan Shahabi; Wei Chen; Khabat Khosravi; Binh Thai Pham; Biswajeet Pradhan; Anuar Ahmad; Baharin Bin Ahmad; Dieu Tien Bui. 2018. "Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping." Sensors 18, no. 11: 3777.
This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.
Dieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Nhat-Duc Hoang; Binh Thai Pham; Quang-Thanh Bui; Chuyen-Trung Tran; Mahdi Panahi; Baharin Bin Ahmad; Lee Saro. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sensing 2018, 10, 1538 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Nhat-Duc Hoang, Binh Thai Pham, Quang-Thanh Bui, Chuyen-Trung Tran, Mahdi Panahi, Baharin Bin Ahmad, Lee Saro. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sensing. 2018; 10 (10):1538.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Nhat-Duc Hoang; Binh Thai Pham; Quang-Thanh Bui; Chuyen-Trung Tran; Mahdi Panahi; Baharin Bin Ahmad; Lee Saro. 2018. "A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides." Remote Sensing 10, no. 10: 1538.
Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.
Dieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Mohsen Alizadeh; Wei Chen; Ayub Mohammadi; Baharin Bin Ahmad; Mahdi Panahi; Haoyuan Hong; Yingying Tian. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia. Remote Sensing 2018, 10, 1527 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Mohsen Alizadeh, Wei Chen, Ayub Mohammadi, Baharin Bin Ahmad, Mahdi Panahi, Haoyuan Hong, Yingying Tian. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia. Remote Sensing. 2018; 10 (10):1527.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Mohsen Alizadeh; Wei Chen; Ayub Mohammadi; Baharin Bin Ahmad; Mahdi Panahi; Haoyuan Hong; Yingying Tian. 2018. "Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia." Remote Sensing 10, no. 10: 1527.
A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC = 0.930). However, RS model (AUROC = 0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC = 0.972), MB (AUROC = 0.970) and AB (AUROC = 0.957) models, respectively.
Mousa Abedini; Bahareh Ghasemian; Ataollah Shirzadi; Himan Shahabi; Kamran Chapi; Binh Thai Pham; Baharin Bin Ahmad; Dieu Tien Bui. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Geocarto International 2018, 34, 1427 -1457.
AMA StyleMousa Abedini, Bahareh Ghasemian, Ataollah Shirzadi, Himan Shahabi, Kamran Chapi, Binh Thai Pham, Baharin Bin Ahmad, Dieu Tien Bui. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Geocarto International. 2018; 34 (13):1427-1457.
Chicago/Turabian StyleMousa Abedini; Bahareh Ghasemian; Ataollah Shirzadi; Himan Shahabi; Kamran Chapi; Binh Thai Pham; Baharin Bin Ahmad; Dieu Tien Bui. 2018. "A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment." Geocarto International 34, no. 13: 1427-1457.
Binh T. Pham; Indra Prakash; Khabat Khosravi; Kamran Chapi; Phan Trong Trinh; Trinh Q. Ngo; Seyed V. Hosseini; Dieu Tien Bui. A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto International 2018, 34, 1385 -1407.
AMA StyleBinh T. Pham, Indra Prakash, Khabat Khosravi, Kamran Chapi, Phan Trong Trinh, Trinh Q. Ngo, Seyed V. Hosseini, Dieu Tien Bui. A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto International. 2018; 34 (13):1385-1407.
Chicago/Turabian StyleBinh T. Pham; Indra Prakash; Khabat Khosravi; Kamran Chapi; Phan Trong Trinh; Trinh Q. Ngo; Seyed V. Hosseini; Dieu Tien Bui. 2018. "A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling." Geocarto International 34, no. 13: 1385-1407.
Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier ensemble method, namely Random Forest Classifier based on Random Subspace Ensemble (RS-RF), for groundwater potential mapping (GWPM) in Qorveh-Dehgolan plain, Kurdistan province, Iran. A total of 12 conditioning factors (slope, aspect, elevation, curvature, stream power index (SPI), topographic wetness index (TWI), rainfall, lithology, land use, normalized difference vegetation index (NDVI), fault density, and river density) were selected for groundwater modeling. The least square support vector machine (LSSVM) feature selection method with a 10-fold cross-validation technique was used to validate the predictive capability of these conditioning factors for training the models. The performance of the RS-RF model was validated using the area under receiver operating characteristic curve (AUROC), success and prediction rate curves, kappa index, and several statistical index-based measures. In addition, Friedman and Wilcoxon signed-rank tests were used to assess statistically significant level among the new model with the state-of-the-art soft computing benchmark models, such as random forest (RF), logistic regression (LR) and naïve Bayes (NB). Results showed that the new hybrid model of RS-RF had a very high predictive capability for groundwater potential mapping and exhibited the best performance among other benchmark models (LR, RF, and NB). Results of the present study might be useful to water managers to make proper decisions on the optimal use of groundwater resources for future planning in the critical study area.
Shaghayegh Miraki; Sasan Hedayati Zanganeh; Kamran Chapi; Vijay P. Singh; Ataollah Shirzadi; Himan Shahabi; Binh Thai Pham. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach. Water Resources Management 2018, 33, 281 -302.
AMA StyleShaghayegh Miraki, Sasan Hedayati Zanganeh, Kamran Chapi, Vijay P. Singh, Ataollah Shirzadi, Himan Shahabi, Binh Thai Pham. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach. Water Resources Management. 2018; 33 (1):281-302.
Chicago/Turabian StyleShaghayegh Miraki; Sasan Hedayati Zanganeh; Kamran Chapi; Vijay P. Singh; Ataollah Shirzadi; Himan Shahabi; Binh Thai Pham. 2018. "Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach." Water Resources Management 33, no. 1: 281-302.