Evaluation of Tailings from a Porphyry Copper Mine based on Joint Simulation of Contaminants     

Sohrabian  Babak  Hosseinzadeh Gharehgheshlagh  Hojjat  Soltani-Mohammadi  Saeed  Abdollahi Sharif  Jafar 《Natural Resources Research》2020,29(2):983-1005

Natural Resources Research - Tailings from porphyry copper mines contain environmentally harmful amounts of elements such as copper, molybdenum, lead and cobalt. Geostatistical simulation of...  相似文献   

A novel approach for ports’ container terminals’ risk management based on formal safety assessment: FAHP-entropy measure—VIKOR model     

Khorram  Saeed 《Natural Hazards》2020,103(2):1671-1707

Natural Hazards - Maritime transport, which requires complex, large, and long port systems, is a major system in the world trade, and since such systems face different risks, various methods have...  相似文献   

Prediction of copper content in waste dump of Sarcheshmeh copper mine using visible and near-infrared reflectance spectroscopy     

Khosravi  Vahid  Ardejani  Faramarz Doulati  Aryafar  Ahmad  Yousefi  Saeed  Karami  Shawgar 《Environmental Earth Sciences》2020,79(7):1-16

The pore structure characteristics of soil are closely related to soil engineering properties. For saline soil distributed in seasonally frozen areas, existing studies have focused on the influence of freeze–thaw cycles on pore structure, while the influence of soluble salt in the soil is not well understood. This study aims to explore the influence of salt content and salt type on the pore structure of freeze-thawed soil. Soil samples with different salt contents (0–2%) and types (bicarbonate salt and sulfate salt) were subjected to 10 freeze–thaw tests, and their pore size distributions (PSDs) were obtained by mercury intrusion porosimetry tests. In addition, the PSDs were quantitatively analyzed by fractal theory. For both salts, the PSDs of the tested soil samples were bimodal after the freeze–thaw cycles, and the porosity of saline soil samples increased with increasing salt content overall. However, the contents of various types of pores in soil samples with two salt types were quite different. The variation in bicarbonate salt content mainly affected the mesopore and macropore contents in the soil samples, and their change trends were opposite to each other. For soil samples with sulfate salt, the porosity and macropore content increased significantly when the salt content exceeded 1%. In addition, the pore structures in saline soil presented fractal characteristics after the freeze–thaw cycles, and the fractal dimension was positively correlated with macropore content. This study may provide references for understanding the engineering properties of saline soil in seasonally frozen areas at the microscale.  相似文献   

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions,a case study in Utah State     

Elnaz Sharghi  Vahid Nourani  Saeed Soleimani  Fahreddin Sadikoglu 《山地科学学报》2018,15(3):461-484

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.  相似文献   

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan     

Ishfaq Ahmad  Umer Saeed  Muhammad Fahad  Asmat Ullah  M. Habib ur Rahman  Ashfaq Ahmad  Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.  相似文献   

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique     

Abbasi  Faezeh  Bazgeer  Saeed  Kalehbasti  Parviz Rezazadeh  Oskoue  Ebrahim Asadi  Haghighat  Masoud  Kalehbasti  Pouya Rezazadeh 《Theoretical and Applied Climatology》2022,147(1-2):47-61

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...  相似文献   

A comparison of frameworks for separating the impacts of human activities and climate change on river flow in existing records and different near-future scenarios     

Soghra Andaryani  Vahid Nourani  James Ball  Saeed Jahanbakhsh Asl  Hamidreza Keshtkar  Dennis Trolle 《水文研究》2021,35(7):e14301

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Burden prediction in blasting operation using rock geomechanical properties     

Mohammad Rezaei  Masoud Monjezi  Saeed Ghorbani Moghaddam  Farhad Farzaneh 《Arabian Journal of Geosciences》2011,5(5):1031-1037

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).  相似文献   

SHRIMP zircon U–Pb and biotite and hornblende Ar–Ar geochronology of Sungun,Haftcheshmeh, Kighal,and Niaz porphyry Cu–Mo systems: evidence for an early Miocene porphyry-style mineralization in northwest Iran     

Shohreh Hassanpour  Saeed Alirezaei  David Selby  Sergey Sergeev 《International Journal of Earth Sciences》2015,104(1):45-59

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Identification of trends in hydrological and climatic variables in Urmia Lake basin,Iran   总被引：1，自引：0，他引：1  

Farshad Fathian  Saeed Morid  Ercan Kahya 《Theoretical and Applied Climatology》2015,119(3-4):443-464

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