排序方式： 共有30条查询结果，搜索用时 62 毫秒
1.
Pinnaduwa H. S. W. Kulatilake Srisharan Shreedharan Taghi Sherizadeh Biao Shu Yan Xing Pengfei He 《Geotechnical and Geological Engineering》2016,34(6):1723-1735
Numerical modeling of complex rock engineering problems involves the use of various input parameters which control usefulness of the output results. Hence, it is of utmost importance to select the right range of input physical and mechanical parameters based on laboratory or field estimation, and engineering judgment. Joint normal and shear stiffnesses are two popular input parameters to describe discontinuities in rock, which do not have specific guidelines for their estimation in literature. This study attempts to provide simple methods to estimate joint normal and shear stiffnesses in the laboratory using the uniaxial compression and small-scale direct shear tests. Samples have been prepared using rocks procured from different depths, geographical locations and formations. The study uses a mixture of relatively smooth natural joints and saw-cut joints in the various rock samples tested. The results indicate acceptable levels of uncertainty in the calculation of the stiffness parameters and provide a database of good first estimates and empirical relations which can be used for calculating values for joint stiffnesses when laboratory estimation is not possible. Joint basic friction angles have also been estimated as by-products in the small scale direct shear tests. 相似文献
2.
Barzegar Rahim Aalami Mohammad Taghi Adamowski Jan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(2):415-433
Stochastic Environmental Research and Risk Assessment - Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely... 相似文献
3.
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
4.
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
5.
Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation 总被引：1，自引：0，他引：1
M. Taghi Sattari Halit Apaydin Fazli Ozturk 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(8):1187-1201
Rainfall distributions in Iran are spatially and temporally heterogeneous, a fact probably linked to the mostly arid and semi-arid
climate of the country. On the other hand, water demand is increasing with increasing population and improving life style.
At present, the optimal utilization of water resources and irrigation dams is the primary concern of water resource managers.
The Eleviyan dam (with a capacity of 60 hm3) was constructed to meet the irrigation and municipal water needs of the Maraghan region (Northwestern Iran). In this study,
the efficiency of the Eleviyan irrigation dam system was investigated in three phases by setting up the optimization model
that maximized the water release for irrigation purposes after municipal water need were met. In the first phase, the inflows
measured in the 21 years prior to the construction of the reservoir, and in the second, the inflows generated by the Monte
Carlo simulation method, and in the third phase, the inflows after the construction of the reservoir were used. The results
demonstrate that the capacity determined during the preliminary studies was accurate and the operation carried out in the
recent periods of operation life was up to a satisfactory standard. 相似文献
6.
Mohsen Abbasnia Taghi Tavousi Mahmood Khosravi 《Asia-Pacific Journal of Atmospheric Sciences》2016,52(4):371-377
Identification and assessment of climate change in the next decades with the aim of appropriate environmental planning in order to adapt and mitigate its effects are quite necessary. In this study, maximum temperature changes of Iran were comparatively examined in two future periods (2041-2070 and 2071-2099) and based on the two general circulation model outputs (CGCM3 and HADCM3) and under existing emission scenarios (A2, A1B, B1 and B2). For this purpose, after examining the ability of statistical downscaling method of SDSM in simulation of the observational period (1981-2010), the daily maximum temperature of future decades was downscaled by considering the uncertainty in seven synoptic stations as representatives of climate in Iran. In uncertainty analysis related to model-scenarios, it was found that CGCM3 model under scenario B1 had the best performance about the simulation of future maximum temperature among all of the examined scenario-models. The findings also showed that the maximum temperature at study stations will be increased between 1°C and 2°C in the middle and the end of 21st century. Also this maximum temperature changes is more severe in the HADCM3 model than the CGCM3 model. 相似文献
7.
Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model 
下载免费PDF全文
下载免费PDF全文 Mohammad Taghi Sattari Rasoul Mirabbasi Reza Shamsi Sushab John Abraham 《Ground water》2018,56(4):636-646
The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively. 相似文献
8.
Simulation of CO<Subscript>2</Subscript> hydrate formation in cold aquifers: nonequilibrium approach
Khaled Jemai Mohammad Taghi Vafaei Bjørn Kvamme Ashok Chejara 《Arabian Journal of Geosciences》2017,10(5):113
In this paper, we focus on the geological storage of CO2 in reservoirs with zones that are cold enough to facilitate CO2 hydrate formation at local pressures. A 2D hydro-chemical mechanical model which has five layers (three layers with aquifers and two layers with cap rock in which we introduced two fractures) is created. We apply a reactive transport reservoir simulator, RetrasoCodeBright (RCB), in which hydrate is treated as a pseudo mineral. Following the recent modifications to account for hydrate dynamics in the code through a kinetic approach (Kvamme et al., Proceedings of the 7th International Conference on Gas Hydrates (ICGH 2011), 2011b), we have further improved the simulator to implement the nonequilibrium thermodynamic calculations. In the present study, we spot the light on the hydrate formation effects on porosity in different regions, as well as on the flow pattern. These simulations are based on classical relationships between porosity and permeability, but the outline of ongoing modifications is presented as well. A critical question in such systems is whether hydrate formation can contribute to stabilizing the storage, given that hydrates are pore filling and cannot be stable toward mineral surfaces. The implications of hydrate formation on the geo-mechanical properties of the model reservoir are other aspects addressed in this study. 相似文献
9.
Shamloo Nazila Sattari Mohammad Taghi Apaydin Halit 《Theoretical and Applied Climatology》2022,149(1-2):39-51
Theoretical and Applied Climatology - Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has... 相似文献
10.
Successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data, and noisy data can affect the modeling performance. On the other hand in training phase of any Artificial Intelligence (AI) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly, in the present paper, wavelet-based denoising method was used to smooth hydrological time series. Thereafter, small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets. Finally, the obtained pre-processed data were imposed into Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models for daily runoff-sediment modeling of the Minnesota River. To evaluate the modeling performance, the outcomes were compared with results of multi linear regression (MLR) and Auto Regressive Integrated Moving Average (ARIMA) models. The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoff-sediment modeling of the case study up to 34% and 25% in the verification phase, respectively. 相似文献