排序方式： 共有73条查询结果，搜索用时 531 毫秒
41.
Shabnam Hasani Omid Asghari Faramarz Doulati Ardejani Saeed Yousefi 《Environmental Earth Sciences》2017,76(15):532
Mining activities and resulting wastes can be considered as one of the most important sources of hazardous elements in the environment. Knowledge of the spatial distribution of toxic elements in waste dump systems is necessary to assess environmental hazard and strategy. To achieve this goal, this paper investigates spatial distribution of toxic elements using statistical and geostatistical analysis. A total of 58 soil samples were collected, and the amount of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb and Zn was then determined at “Sarcheshmeh” copper mine waste dumps. In order to evaluate the presence of multivariate outliers, Mahalanobis distance technique (D 2) was applied and the multivariate outlier samples were removed. This resulted in an increase in correlation coefficient. To reduce dimension of data set, principal component analysis was applied and four principal components were determined which indicate 83.463% of the total variance of data set. Estimated PCs together with the toxic elements maps based on the ordinary kriging display aggregation of toxic elements in some parts, and validity of predictions was evaluated using the leave-one-out cross-validation method. The regression coefficients of estimated and observed values presented the reliability of the kriging estimates. Sequential Gaussian simulation method was applied for principal components due to similar results of estimated principal components and toxic elements. The results of simulation maps are almost identical to estimated outcomes. 相似文献
42.
A deterministic monthly runoff model (MINRUN96)was applied to watersheds with substantially differentclimates. One watershed is in the north-central U.S.(Minnesota) and is heavily timbered. The other is inthe south-central U.S. (Oklahoma) and is mainlycovered with pastures and agricultural crops. Runoffwas simulated for past historical climate and twoprojected 2 × CO2 climate scenarios. The output ofGeneral Circulation Models (GCMs) was used to specifythe two 2 × CO2 climate scenarios. One GCM is theGoddard Institute of Space Studies (GISS) model andthe other is from the Canadian Center of ClimateModelling (CCC). In the northern watershed morerunoff is projected to occur in winter under a warmerclimate and less runoff in spring. About 80%increase in fall runoff and 20% decrease in soilmoisture in June and July is projected for thesouthern watershed. When runoff simulations for the2 × CO2 climate scenarios were compared to pastrunoff, it was apparent that the change in runoffdepended on both the season and the magnitude of theprecipitation change. An increase in springprecipitation caused a significant increase in directrunoff, whereas an increase in fall precipitationcaused only a slight increase in total runoff. Alsothe runoff-precipitation relationship in the warm andseasonally dry southern watershed is very differentfrom that in the temperate and humid climate of thenorth. Therefore, runoff responses to projectedclimate change are substantially different in the tworegions. 相似文献
43.
To project potential habitat changes of 57 fish species under global warming, their suitable thermal habitat at 764 stream gaging stations in the contiguous United States was studied. Global warming was specified by air temperature increases projected by the Canadian Centre of Climate Modelling General Circulation Model for a doubling of atmospheric CO2. The aquatic thermal regime at each gaging station was related to air temperature using a nonlinear stream temperature/air temperature relationship.Suitable fish thermal habitat was assumed to be constrained by both maximum temperature and minimum temperature tolerances. For cold water fishes with a 0 °C lower temperature constraint, the number of stations with suitable thermal habitat under a 2×CO2 climate scenario is projected to decrease by 36%, and for cool water fishes by 15%. These changes are associated with a northward shift of the range. For warm water fishes with a 2 °C lower temperature constraint, the potential number of stations with suitable thermal habitat is projected to increase by 31%. 相似文献
44.
Phuong Thao Thi Ngo Mahdi Panahi Khabat Khosravi Omid Ghorbanzadeh Narges Kariminejad Artemi Cerda Saro Lee 《地学前缘(英文版)》2021,12(2):505-519
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies. 相似文献
45.
Sun Qingfeng Zamanian Kazem Huguet Arnaud Bayat Omid Wang Hong Badawy Hanan S. 《中国地球化学学报》2022,41(5):811-822
Acta Geochimica - Desert rhizoliths are generally found as weathered, broken and scattered samples on dune field surface, but rarely in-situ in their initial states buried under the soil of desert... 相似文献
46.
Bulletin of Earthquake Engineering - A structure may be subject to several aftershocks after a mainshock. In many seismic design provisions, the effect of the seismic sequences is either not... 相似文献
47.
Most approaches in statistical spatial prediction assume that the spatial data are realizations of a Gaussian random field.
However, this assumption is hard to justify for most applications. When the distribution of data is skewed but otherwise has
similar properties to the normal distribution, a closed skew normal distribution can be used for modeling their skewness.
Closed skew normal distribution is an extension of the multivariate skew normal distribution and has the advantage of being
closed under marginalization and conditioning. In this paper, we generalize Bayesian prediction methods using closed skew
normal distributions. A simulation study is performed to check the validity of the model and performance of the Bayesian spatial
predictor. Finally, our prediction method is applied to Bayesian spatial prediction on the strain data near Semnan, Iran.
The mean-square error of cross-validation is improved by the closed skew Gaussian model on the strain data. 相似文献
48.
Omid Saeidi Seyed Rahman Torabi Mohammad Ataei 《Rock Mechanics and Rock Engineering》2014,47(2):717-732
Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools. 相似文献
49.
Talesh Hosseini Sajjad Asghari Omid Benndorf Jörg Emery Xavier 《Mathematical Geosciences》2023,55(4):541-562
One main problem in the modeling of mineral deposits is to design a block model that divides the deposit into homogeneous subdomains. The spatial uncertainty in the geological boundaries becomes a critical factor prior to the modeling of the ore properties. For this reason, reducing the uncertainty of geological models leads to an improved mineral resource evaluation. This research work addresses the problem of updating the geological models by using actual online-sensor measurement data. A novel algorithm is provided, which integrates the discrete wavelet transform to the Ensemble Kalman Filter for assimilating online-sensor production data into geological models. The geological realizations in each time step are transformed to frequency coefficients and, after each assimilation step, the updated realizations are back-transformed to the original categorical distribution. Furthermore, a reconciliation process is performed to compare the online-sensor data derived from the production blocks and the updated realizations in each time step. The algorithm is illustrated through an application to the Golgohar iron deposit located in SW of Sirjan, Iran, and proves to reproduce the statistical parameters and connectivity values of the primary geological realizations.
相似文献50.
Nalivan Omid Asadi Badehian Ziaedin Sadeghinia Majid Soltani Adel Islami Iman Boustan Ali 《Natural Hazards》2022,111(2):1661-1684
Natural Hazards - In an effort to improve the previous gully susceptibility assessments in Iran, we attempted to conglomerate the notions of susceptibility, vulnerability, and exposure associated... 相似文献