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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
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The Iran’s mineral sector, as a major supplier of mineral industries, plays a central role on Iran economy. Promoting the productivity in this area leads to the enhancement of the business environment. Also, it can influence the production chain and minerals value added. In this study, a comprehensive study was conducted on the major barriers/drivers factors affecting the efficiency in the mining sector. Furthermore, 16 key factors were identified using the expert views. These parameters are divided into four main categories: strengths, weaknesses, opportunities, and threats. In addition, a hierarchy model was designed in which the mentioned four efficient strategic parameters were implemented and the SWOT analysis was applied, as well as the nine macro strategies were determined. In order to prioritize these strategies, multiple steps were carried out. First, the subfactors were weighted by implementing the fuzzy multi attribute decision-making technique by combining the experts’ views and triangular fuzzy numbers. Subsequently, the strategies were prioritized by employing the group Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The sensitive analysis of the results presents some minor changes in score of the alternatives, but no alteration was affected in the prioritized strategies. The results revealed the importance of resource allocation strategies related to the existing investments as well as supporting and facilitating the new technology in the mining sector. 相似文献
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Ghorbanzadeh Omid Shahabi Hejar Crivellari Alessandro Homayouni Saeid Blaschke Thomas Ghamisi Pedram 《Landslides》2022,19(4):929-939
Landslides - Recent landslide detection studies have focused on pixel-based deep learning (DL) approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on... 相似文献
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Mousa Abedini Bahareh Ghasemian Ataollah Shirzadi Himan Shahabi Kamran Chapi Binh Thai Pham 《国际地球制图》2013,28(13):1427-1457
AbstractA 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
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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
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We study FRW cosmology for a double scalar-tensor theory of gravity where two scalar fields are nonminimally coupled to the
geometry. In a framework to study stability and attractor solutions of the model in the phase space, we constrain the model
parameters with the observational data. For an accelerating universe, the model behaves like quintom dark energy models and
predicts a transition from quintessence era to phantom era. 相似文献
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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
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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
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