A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

Hydrodynamic Tidal Model of the Persian Gulf Based on Spatially Variable Bed Friction Coefficient

The aim of this study is to develop a two-dimensional hydrodynamic tidal model for the Persian Gulf (PG2017) using 2D-MIKE21 software. The advantages of present study is accounting for the spatial variation of bed friction coefficient besides a precise bathymetry together with a 23-year of combined records of satellite altimetry data. We found that the bed friction coefficient has a significant effect on sea level changes in the region under our modeling consideration. Since the tidal behavior in the northern part of the Qeshm Island is significantly different from the other parts of the Persian Gulf, to present a more accurate hydrodynamic tidal model, the Gulf is divided into two regions where the bed friction coefficient is modeled separately for each region. The root mean square value of the differences between the amplitude of dominant constituents; M2, S2, K1, and O1 derived from the PG2017 model and that of 98 altimetry and coastal tide gauge stations are respectively equal to 1.6, 1.9, 2.8, and 1.3?cm. Moreover, comparing the PG2017 model efficiency with the FES2014, OSU12, EOT11a, DTU10, and Admiralty models shows that the PG2017 model has an improvement of 22.1%, 47.2%, 43.2%, 44.2%, and 57.6% in terms of relative error, respectively.     

Optimization of Flue Gas Composition to Maximize Base Gas Replacement During UGS Process

Natural Resources Research - Base gas replacement by a cheap gas is one of the approved methods to reduce the cost of investment in underground gas storage process. Maximizing the amount of...     

Prediction of the median IDA curve by employing a limited number of ground motion records

A methodology has been proposed which can be used to reduce the number of ground motion records needed for the reliable prediction of the median seismic response of structures by means of incremental dynamic analysis (IDA). This methodology is presently limited to predictions of the median IDA curve only. The reduction in the number of ground motion records needed to predict the median IDA curve is achieved by introducing a precedence list of ground motion records. The determination of such a list is an optimization problem, which is solved in the paper by means of (1) a genetic algorithm and (2) a proposed simple procedure. The seismic response of a simple, computationally non‐demanding structural model has been used as input data for the optimization problem. The presented example is a three‐storey‐reinforced concrete building, subjected to two sets of ground motion records, one a free‐field set and the other a near‐field set. It is shown that the median IDA curves can be predicted with acceptable accuracy by employing only four ground motion records instead of the 24 or 30 records, which are the total number of ground motion records for the free‐field and near‐field sets, respectively. Copyright © 2007 John Wiley & Sons, Ltd.     

The influence of the Arctic Oscillation on winter temperatures in Iran

Summary The impact of the Arctic Oscillation (AO) on the winter surface air temperature (SAT) over Iran is demonstrated. Winter SAT data for 50 years (1951–2000) are analyzed for the negative and the positive AO phases. Using the Median Sequential Correlation Analysis (MSCA) technique it is shown that the winter SAT is negatively correlated to the winter AO index for most parts of Iran. The winter AO index accounts for about 14% to 46% of the winter SAT variance. The positive (negative) SAT anomaly is found to be associated with the onset of the negative (positive) phase. The overall probability of below long-term mean temperature during the positive and the negative phases are estimated to be around 70% and 25%, respectively. For the negative phase, westerly winds that originate from the warm Atlantic regions increase over Iran and consequently positive temperature anomalies are found across the country. The positive AO phase is accompanied by northerly winds that allow continental polar and arctic air masses to move into Iran, producing below normal temperatures. The summer AO is found to explain about 25–32% of the winter SAT variance in Iran. The reason for this is explained by the significant correlation (+0.38) between the summer and the following winter AO indices. These results indicate that the summer climate is linked to changes in atmospheric circulation which persist through to the following autumn and winter.     

Thermal instability in ionized plasma

We study the magneto-thermal instability in ionized plasmas including the effects of Ohmic, ambipolar and Hall diffusion. The magnetic field in the single-fluid approximation does not allow for transverse thermal condensations; however, non-ideal effects highly diminish the stabilizing role of the magnetic field in thermally unstable plasmas. Therefore, the enhanced growth rate of thermal condensation modes in the presence of the diffusion mechanisms speed up the rate of structure formation.     

Investigation of surface and subsurface displacements due to multiple tunnels excavation in urban area

Underground structures are currently widely used and are built as urbanism develops. The interactions between perpendicularly crossing and parallel tunnels in the Tehran region are investigated by using a full three-dimensional (3D) finite difference analysis with elastic-plastic material models. Special attention is paid to the effect of subsequent tunneling on the support system, i.e., the shotcrete lining and rock bolts of the existing tunnel. Eventually, as the tunnels are excavated at certain levels, the interaction between the tunnels will certainly have a significant influence on both stress distribution and consequently deformations. Since multilayer tunneling is a three-dimensional phenomenon in nature, 3D numerical solutions must be utilized for analyzing effect of perpendicularly crossing tunnels at various levels. As Tohid twin tunnels and Line 7 pass beneath the Line 4 metro tunnel, changes in stress distribution, deformations, and surface settlements are studied for various conditions and the results are presented in this paper. Consequently, it is shown that there is a significant interaction between tunnels that necessitate certain preventive measures to maintain a stable tunneling operation.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.