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11.
Vahid Karkehabadi Hamidreza Zarei Ali Uromeihy Hamidreza Karkehabadi 《Geotechnical and Geological Engineering》2014,32(5):1353-1362
Nowadays, the world is witnessing the ever increasing need of Tunnel excavation due to their unique features and the kind of human applied plans. This has led to increase in demand of excavating this engineering factor. Tunnel excavation process faces a lot of challenges due to environmental and technological complexities which causes the economic evaluation and investigation of this project to be difficult. It is tried to develop the proposed model with regard to efficiency concept in order to evaluate and investigate the efficiency of relative economic performance of Tunnel excavation projects and turn to its modeling and implementing by data envelopment analysis and Fuzzy DEMATEL techniques. The results in Iran showed that the proposed model can turn to investigation and evaluation of economic efficiency of Tunnel excavation by considering two optimistic (ideal) and pessimistic perspectives such that the Tunnel excavation process of “Karaj water transition” and “Cheshmelangan water transition” among 12 rock Tunnel excavation projects of Iran in the time period of 1998–2013 were respectively introduced as the most efficient and the most inefficient rock Tunnel excavation projects. 相似文献
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This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Water Resources - Monitoring and reviewing vegetation changes are one of the important strategies for multi-year planning in an area regarding the environmental problems of the last decade. The... 相似文献
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Moosavi Seyed Amin Bakhtiari Hesam Aloki Honarmand Javad 《Geotechnical and Geological Engineering》2022,40(6):3225-3244
Geotechnical and Geological Engineering - This study examined the petrographically classification, petrological and petrophysical characteristics by taking a vast range of carbonate reservoir rock... 相似文献
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Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting. 相似文献
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Vahid Mahboub Mohammad Saadatseresht Alireza A. Ardalan 《Studia Geophysica et Geodaetica》2017,61(1):19-34
An applicable algorithm for Total Kalman Filter (TKF) approach is proposed. Meanwhile, we extend it to the case in which we can consider arbitrary weight matrixes for the observation vector, the random design matrix and possible correlation between them. Also the updated dispersion matrix of the predicted unknown is given. This approach makes use of condition equations and straightforward variance propagation rules. It is applicable to data fusion within a dynamic errors-in-variables (DEIV) model, which usually appears in the determination of the position and attitude of mobile sensors. Then, we apply for the first time the TKF algorithm and its extended version named WTKF to a DEIV model and compare the results. The results show the efficiency of the proposed WTKF algorithm. In particular in the case of large weights, WTKF shows approximately 25% improvement in contrast to TKF approach. 相似文献
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Vahid?NouraniEmail authorView author&#;s OrcID profile Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562
Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence 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 article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds. 相似文献
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Maziar Khosravi Seyed Mostafa Siadatmousavi Ross Vennell Vahid Chegini 《Ocean Dynamics》2018,68(2):239-254
Vessel-mounted ADCP measurements were conducted to describe the transverse structure of flow between the two headland tips in Khuran Channel, south of Iran (26° 45′ N), where the highest tidal velocities in spring tides were ~?1.8 m/s. Current profiles were obtained using a 614.4 kHz TRDI WorkHorse Broadband ADCP over nine repetitions of three cross-channel transects during one semidiurnal tidal cycle. The 2.2-km-long transects ran north/south across the channel. A least-square fit to semidiurnal, quarter-diurnal, and sixth diurnal harmonics was used to separate the tidal signals from the observed flow. Spatial gradients showed that the greatest lateral shears and convergences were found over the northern channel and near the northern headland tip due to very sharp bathymetric changes in this area. Contrary to the historical assumption, the across-channel momentum balance in the Khuran Channel was ageostrophic. The current study represents one of the few examples reported where the lateral friction influences the across-channel momentum balance. 相似文献
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Acta Geotechnica - Evidence from recent earthquakes reminds us that fault-induced permanent ground displacement has a devastating effect on structures in addition to damage caused by wave... 相似文献