Erratum to: Development and validation of a novel-based combination operational air quality forecasting system in Greece     

Stamatis Zoras  Vasilis Evagelopoulos  Ioannis Pytharoulis  Athanasios G. Triantafyllou  Ioannis Skordas  Georgios Kallos 《Meteorology and Atmospheric Physics》2014,125(1-2):103-103

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Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization     

Mohammad Ali Ahmadi  Sohrab Zendehboudi  Ali Lohi  Ali Elkamel  Ioannis Chatzis 《Geophysical Prospecting》2013,61(3):582-598

Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.  相似文献   

Location Performance and Detection Threshold of the Spanish National Seismic Network     

Antonino D’Alessandro  José Badal  Giuseppe D’Anna  Dimitris Papanastassiou  Ioannis Baskoutas  Nurcan M. Özel 《Pure and Applied Geophysics》2013,170(11):1859-1880

Spain is a low-to-moderate seismicity area with relatively low seismic hazard. However, several strong shallow earthquakes have shaken the country causing casualties and extensive damage. Regional seismicity is monitored and surveyed by means of the Spanish National Seismic Network, maintenance and control of which are entrusted to the Instituto Geográfico Nacional. This array currently comprises 120 seismic stations distributed throughout Spanish territory (mainland and islands). Basically, we are interested in checking the noise conditions, reliability, and seismic detection capability of the Spanish network by analyzing the background noise level affecting the array stations, errors in hypocentral location, and detection threshold, which provides knowledge about network performance. It also enables testing of the suitability of the velocity model used in the routine process of earthquake location. To perform this study we use a method that relies on P and S wave travel times, which are computed by simulation of seismic rays from virtual seismic sources placed at the nodes of a regular grid covering the study area. Given the characteristics of the seismicity of Spain, we drew maps for M _L magnitudes 2.0, 2.5, and 3.0, at a focal depth of 10 km and a confidence level 95 %. The results relate to the number of stations involved in the hypocentral location process, how these stations are distributed spatially, and the uncertainties of focal data (errors in origin time, longitude, latitude, and depth). To assess the extent to which principal seismogenic areas are well monitored by the network, we estimated the average error in the location of a seismic source from the semiaxes of the ellipsoid of confidence by calculating the radius of the equivalent sphere. Finally, the detection threshold was determined as the magnitude of the smallest seismic event detected at least by four stations. The northwest of the peninsula, the Pyrenees, especially the westernmost segment, the Betic Cordillera, and Tenerife Island are the best-monitored zones. Origin time and focal depth are data that are far from being constrained by regional events. The two Iberian areas with moderate seismicity and the highest seismic hazard, the Pyrenees and Betic Cordillera, and the northwestern quadrant of the peninsula, are the areas wherein the focus of an earthquake is determined with an approximate error of 3 km. For M _L 2.5 and M _L 3.0 this error is common for almost the whole peninsula and the Canary Islands. In general, errors in epicenter latitude and longitude are small for near-surface earthquakes, increasing gradually as the depth increases, but remaining close to 5 km even at a depth of 60 km. The hypocentral depth seems to be well constrained to a depth of 40 km beneath the zones with the highest density of stations, with an error of less than 5 km. The M _L magnitude detection threshold of the network is approximately 2.0 for most of Spain and still less, almost 1.0, for the western sector of the Pyrenean region and the Canary Islands.  相似文献   

Seismic reliability assessment of classical columns subjected to near‐fault ground motions     

Ioannis N. Psycharis  Michalis Fragiadakis  Ioannis Stefanou 《地震工程与结构动力学》2013,42(14):2061-2079

A methodology for the performance‐based seismic risk assessment of classical columns is presented. Despite their apparent instability, classical columns are, in general, earthquake resistant, as proven from the fact that many classical monuments have survived many strong earthquakes over the centuries. Nevertheless, the quantitative assessment of their reliability and the understanding of their dynamic behavior are not easy, because of the fundamental nonlinear character and the sensitivity of their response. In this paper, a seismic risk assessment is performed for a multidrum column using Monte Carlo simulation with synthetic ground motions. The ground motions adopted contain a high‐ and low‐frequency component, combining the stochastic method, and a simple analytical pulse model to simulate the directivity pulse contained in near source ground motions. The deterministic model for the numerical analysis of the system is three‐dimensional and is based on the Discrete Element Method. Fragility curves are produced conditional on magnitude and distance from the fault and also on scalar intensity measures for two engineering demand parameters, one concerning the intensity of the response during the ground shaking and the other the residual deformation of the column. Three performance levels are assigned to each engineering demand parameter. Fragility analysis demonstrated some of the salient features of these spinal systems under near‐fault seismic excitations, as for example, their decreased vulnerability for very strong earthquakes of magnitude 7 or larger. The analysis provides useful results regarding the seismic reliability of classical monuments and decision making during restoration process. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization     

Evdokia Tapoglou  Ioannis C. Trichakis  Zoi Dokou  Ioannis K. Nikolos 《水文科学杂志》2013,58(6):1225-1239

Abstract

Artificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.