Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis     

Chen  Wei  Pradhan  Biswajeet  Li  Shaojun  Shahabi  Himan  Rizeei  Hossein Mojaddadi  Hou  Enke  Wang  Shengquan 《Natural Resources Research》2019,28(4):1239-1258

Natural Resources Research - Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of...  相似文献   

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment     

Mousa Abedini  Bahareh Ghasemian  Ataollah Shirzadi  Himan Shahabi  Kamran Chapi  Binh Thai Pham 《国际地球制图》2013,28(13):1427-1457

Abstract

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.  相似文献   

Landslide susceptibility assessment at the Wuning area,China: a comparison between multi-criteria decision making,bivariate statistical and machine learning methods     

Hong  Haoyuan  Shahabi  Himan  Shirzadi  Ataollah  Chen  Wei  Chapi  Kamran  Ahmad  Baharin Bin  Roodposhti  Majid Shadman  Yari Hesar  Arastoo  Tian  Yingying  Tien Bui  Dieu 《Natural Hazards》2019,96(1):173-212

Natural Hazards - 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...  相似文献   

Evaluating Boolean,AHP and WLC methods for the selection of waste landfill sites using GIS and satellite images   总被引：2，自引：2，他引：0  

Himan Shahabi  Soroush Keihanfard  Baharin Bin Ahmad  Mohammad Javad Taheri Amiri 《Environmental Earth Sciences》2014,71(9):4221-4233

The city of Saqqez has a population of 140,000 people, making it one of the largest cities in Iran. Population growth, consumerism, and change in eating habits, such as the increased use of packaged products, is causing the accumulation of waste in this city to increase. In this study, the selection of a waste landfill site for Saqqez focused on 13 layers of geography information that was used by the IDRISI and Arc GIS software. Different models of the analytic multi-criteria decision-making process, such as an analytical hierarchy process (AHP), weighted linear combination (WLC), and Boolean logic, were used to manage layers to establish specific databases for urban waste landfills. Satellite images (Landsat ETM⁺ and SPOT 5), proposed sites and a land use map of the study area were also used. The results of this study indicated that two methods (AHP and WLC) in the early stages had better decision-making powers for locating landfill sites when compared to Boolean logic. Overlapping and compounding the similarities between these models in Arc GIS software, a 74-ha site was found. This site will be able to accept 130 tons of waste per day for the next 20 years.  相似文献   

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)     

Haoyuan Hong  Junzhi Liu  A-Xing Zhu  Himan Shahabi  Binh Thai Pham  Wei Chen  Biswajeet Pradhan  Dieu Tien Bui 《Environmental Earth Sciences》2017,76(19):652

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.  相似文献