排序方式： 共有48条查询结果，搜索用时 46 毫秒
1.
S. Abarghoei A. Hedayati R. Ghorbani H. K. Miandareh T. Bagheri 《International Journal of Environmental Science and Technology》2016,13(7):1753-1760
This study aimed to compare histopathological effect of waterborne silver nanoparticles and silver salt (AgNO3) on the gills and liver of Carassius auratus. Therefore, one hundred and five live specimens of goldfish were obtained and treated in five aquariums with 0, 0.01, 0.025, 0.05, and 0.1 ppm of AgNO3 and 0, 0.1, 0.5, 1, and 5 ppm of Ag nanoparticles (mean particle size of 5 nm). Fish were sampled after 14 days of exposure. Results showed that the kinds of pathologies observed with Ag NPs were broadly of the same type as AgNO3 including hyperplasia, edema and lifting of the gill epithelium, and lamellar fusion of the gills, and hemosiderosis, hemorrhage, hydropic swelling, and pyknotic nuclei of the liver. Overall, the data showed that although Ag nanoparticles and AgNO3 pathology were similar, but Ag nanoparticles caused less injury than AgNO3 in the gills and liver of goldfish. Therefore, it is more proper to use nanoform of Ag in industrials. 相似文献
2.
Behnam Ghorbani Ehsan Sadrossadat Jafar Bolouri Bazaz Parisa Rahimzadeh Oskooei 《Geotechnical and Geological Engineering》2018,36(4):2057-2076
This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (Pu) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of Pu based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is Pu for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of Pu with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done. 相似文献
3.
Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models 总被引：5，自引：0，他引：5
The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m3s?1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose. 相似文献
4.
Bahare Raheli Mohammad Taghi Aalami Ahmed El-Shafie Mohammad Ali Ghorbani Ravinesh C. Deo 《Environmental Earth Sciences》2017,76(14):503
Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems. 相似文献
5.
A common assumption in analyzing spatial and spatio-temporal point processes is stationarity, while in many real applications because of the environmental effects the stationarity condition is not often met. We propose two types of test statistics to test stationarity for spatio-temporal point processes, by adapting, Palahi, Pukkala & Mateu (2009) and by considering the square difference between observed and expected (under stationarity) intensities. We study the efficiency of the new statistics by simulated data, and we apply them to test the stationarity of real data. 相似文献
6.
Rahman Khatibi Mohammad Ali Ghorbani Mohammad Taghi Aalami Kasim Kocak Oleg Makarynskyy Dina Makarynska Mahdi Aalinezhad 《Ocean Dynamics》2011,61(11):1797-1807
Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive
management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys
Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed:
low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which
describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem
suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation
dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h
or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific
dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another,
the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight
from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual
insights provided by each model. 相似文献
7.
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
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9.
Mahsa H. Kashani Mohammad Ali Ghorbani Yagob Dinpasho Sedaghat Shahmorad Zbigniew W. Kundzewicz 《Water Resources》2017,44(4):568-578
Although the Volterra models are non-parsimonious ones, they are being used because they can mimic dynamics of complex systems. However, applying and identification of the Volterra models using data may result in overfitting problem and uncertainty. In this investigation we evaluate capability of different wavelet forms for decomposing and compressing the Volterra kernels in order to overcome this problem by reducing the number of the model coefficients to be estimated and generating smooth kernels. A simulation study on a rainfall?runoff process over the Cache River watershed showed that the method performance is successful due to multi-resolution capacity of the wavelet analysis and high capability of the Volterra model. The results also revealed that db2 and sym2 wavelets have the same high potential in improving the linear Volterra model performance. However, QS wavelet was more successful in yielding smooth kernels. Moreover, the probability of overfitting while identifying the nonlinear Volterra model may be less than the linear model. 相似文献
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