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In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.  相似文献   
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Natural Hazards - Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based...  相似文献   
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This study uses a semi‐active viscous damper with three different control laws to reshape the structural hysteresis loop and mitigate structural response, referred to as 1–4, 1–3 and 2–4 devices, respectively. The 1–4 control law provides damping in all four quadrants of the force‐displacement graph (it behaves like a standard viscous damper), the 1–3 control law provides resisting forces only in the first and third quadrants, and the 2–4 control law provides damping in the second and fourth quadrants. This paper first outlines the linear single degree of freedom structural performance when the three types of semi‐active viscous dampers are applied. The results show that simultaneous reduction in both displacement and base‐shear demand is only available with the semi‐active 2–4 device. To enable guidelines for adding a 2–4 device into the design procedure, damping reduction factors (RFξs) are developed, as they play an important role and provide a means of linking devices to design procedures. Three methods are presented to obtain RFξ and equivalent viscous damping of a structure with a 2–4 semi‐active viscous damper. In the first method, the relationship between RFξ and the damping of a semi‐active structure can be obtained by calculating the area under the force‐deformation diagram. The second and third method modified the Eurocode8 formula of RFξ and smoothed results from analysis, respectively. Finally, a simple method is proposed to incorporate the design or retrofit of structures with simple, robust and reliable 2–4 semi‐active viscous dampers using standard design approaches. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   
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Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.  相似文献   
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ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.  相似文献   
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This study focuses on non-linear seismic response of concrete gravity quay-wall structures subjected to near-fault ground motions, a subject which seems not to have received much attention in the literature. A two-dimensional coupled fluid-structure-soil finite element modelling is employed to obtain the quay-wall response. The seawater medium is represented by acoustic type, potential based fluid elements. The elasto-plastic behavior of the soil medium is idealized using Drucker-Prager yield criterion based on associated flow rule assumption. Four nodded plane strain elements are used to model the concrete wall, foundation, subsoil, backfill and seabed zones. Fluid Structure Interface (FSI) elements are considered between the seawater interfaces with the quay-wall and the seabed. Frictional contact elements are employed between the wall and soil interfaces. The numerical model is validated using field measurements available for permanent drifts in a quay-wall damaged during Kobe earthquake. Reasonable agreements are obtained between the model predictions and the field measurements. Non-linear seismic analyses of the selected quay-wall subjected to both near-fault and far-fault ground motions are performed. An incremental dynamic analysis approach (IDA) is used. In general, at least for models examined in the current study, the gravity quay-walls are found to be more vulnerable to near-field, in comparison with the corresponding far-field, earthquakes.  相似文献   
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Due to anthropogenic influences and large amounts of pollutant released into the groundwater, it is vital to investigate groundwater quality and to characterize susceptible areas to contamination. In this paper, a new optimization-based methodology is proposed for determining groundwater risk using DRASTIC model based on genetic algorithm optimization model and Wilcoxon test. The correlation coefficient between DRASTIC/modified DRASTIC indices and nitrate concentrations in monitoring wells is used as a criteria for evaluating the efficiency of the proposed models. In this regard, because of the unsatisfactory original DRASTIC’s result, sensitivity analysis, genetic algorithm (GA), and Wilcoxon test (1945) are carried out to tackle the subjectivity associated with the original DRASTIC model and obtain better and reliable results. The results indicate that application of Wilcoxon test and GA optimization outperforms the others. Consequently, the correlation coefficient increased remarkably as compared to the original DRASTIC model (from 0.57 to 0.82). The proposed optimization process is adaptable to be applied in different case studies; mainly since it has the ability to optimize the weights of the model based on hydrogeological characteristics of the aquifer. Finally, the risk maps of the models are prepared using ArcGIS® to determine the most vulnerable areas.  相似文献   
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In many parts of Canada, limited data are available for hydrodynamic model inputs, and the ability to generate quality flood grids through 1D, 2D or 3D methods is nonviable. In this paper, the capability of simplified flood models, which rely solely on digital terrain models (DTMs), was explored to assess the quality and speed of their results. Results were validated against historic floods in two locations. Three non-physics-based simplified conceptual flood models were tested: (1) planar method, (2) inclined plane and (3) height above nearest drainage network (HAND) model. The accuracy and performance were evaluated using three criteria: inundation extent, water depth and computation time. Findings show that the HAND model is the best predictor of inundation extent, with Probability of Detection and Critical Success Index being higher than 0.90 in both study areas. Though the preprocessing time for the HAND model is lengthy, once completed, the time to simulate flooding at a variety of water levels is rapid, making this model the most suitable choice for web-based, on-demand flood inundation mapping. Knowledge of the fit of these flood models and associated uncertainty can be helpful to emergency managers such that they can better understand exposure and vulnerability while preparing flood response plans.  相似文献   
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