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Abstract: Various studies have been conducted thus far to classify the seismicity regions of Iran into zones that share similar seismic characteristics. In these studies, seismotectonic models have been presented that implement various information layers based mostly on human expert judgment. The purpose of the present research is to develop a seismotectonic model using all of the available information about the layers along with an automated smart clustering algorithm. In this paper, based on a review of past research studies, a combined model is presented, which involves the combination of the four most-referenced seismotectonic models. To build upon past studies, a smart algorithm called the self organizing map (SOM) was selected for clustering the seismotectonic data of provinces. Input layers, such as earthquake coordinates, seismicity, faults distribution, topography, gravity, and magnetism, were used to execute the SOM smart algorithm. Based upon previous studies, the number of clusters was selected in the range of 5–25. Three of the SOM algorithm outputs, which were more compatible with the combined model, were selected for use in this study. According to the clustering validation methods and expert judgments, the highest compatibility belongs to the models that were produced either by all of the information or by the combination of seismicity, faults, and topography layers. Implementation of the clustering validation methods indicated that the optimum number of seismotectonic zones is between 9 and 13. This study emphasizes that the application of smart methods along with various geological, structural, subsurface, and aerial information layers will produce informative and reliable results. [Copyright &y& Elsevier]

The railroad blocking problem (RBP) is an important decision for freight railroad companies. The objective of this problem is to minimize the costs of delivering all commodities by deciding which interyard blocks to build and specifying the assignment of commodities to these blocks. In this paper, a mathematical model is presented for the RBP on Iran Railways. Its decision variables identify blocking scheme and demand assignment to these blocks. The RBP in medium and large sizes is not solvable with any commercial software available in the market. Therefore, a solution method based on tabu search algorithm is proposed for the suggested model. For evaluating the proposed algorithm, several simulated test problems are randomly generated and solved. The obtained results on the test problems are compared with those of solutions generated by CPLEX software. The comparison shows high efficiency and effectiveness of the proposed algorithm. The proposed model and solution method are applied to build a blocking plan for the Iranian railway. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user''s experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (Pb) and the oil formation volume factor at bubble point (Bob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions. [Copyright &y& Elsevier]

Land surface temperature (LST) is a significant parameter for many applications. Many studies have proposed various algorithms, such as the split-window method, for retrieving surface temperatures from two spectrally adjacent thermal infrared bands of satellite data. Each algorithm is developed for a limited study area and application. In this paper, as part of developing an optimal split-window method in the southeast of Tehran province, Iran, four commonly applied algorithms to retrieve the LST from AVHRR were compared. This study was carried out in a wheat farm site located in the Pakdasht Agricultural Region. Measurements of LST over the farm were made with a manual infrared radiometer at the time of NOAA overpass for 18 days of May to June 2004. These days were cloud free over the study area. A total of 18 NOAA images were acquired for the days that LST measurements were made. The temperatures derived by the different split-window algorithms were compared to ground truth measurements. The performance of the split window algorithms was checked with three statistical indices: root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2). The results showed that the Ulivieri split-window algorithm produced the lowest value of RMSE and MBE (2.71 and 0.26 K, respectively) and its highest value of R2 (0.92) gave more accurate results than the other algorithms. [ABSTRACT FROM AUTHOR]

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]