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Predictions of sediment load are required in a wide spectrum of problems such as design of the dead volume of a dam, sediment transport in the river, design of stable channels, and dredging needs. Researchers have used the regression between sediment concentration and water discharge. Such relationships are obtained through the application of regression analysis in many studies. Unfortunately, in the classical regression approach to determine sediment concentration-water discharge relationships, internal uncertainties are not taken explicitly into consideration. Therefore, researchers look for non-linear methods to estimate sediment load such as artificial neural network (ANN) methods to solve non-linear problems. The main purpose of this paper is to optimize the ANN connection weights with novel social-based algorithm (SBA) to realize the sediment in Maroon river. The SBA tries to capture several people in different types of countries. They try to reach high levels. The approach illustrated feed-forward neural network optimization for sediment estimation of four Maroon river stations in Iran, which was called FF-SBA. Three inputs were presented in each station: length of river, discharge and debit. Sediment parameter on the same station is measured as the output parameter. Results of optimization algorithms such as the genetic algorithm, particle swarm optimization and imperialist competitive algorithm were compared with the SBA results, and it was found that the FF-SBA model exhibited more capability, flexibility, and accuracy in sediment training, testing, and forecasting steps for the Maroon river in Iran. [ABSTRACT FROM AUTHOR]

The current paper deals with a new use of fuzzy logic in the domain of seismic zonation methodologies with a case study in Meybod, Iran. Determining the risk function of an earthquake is an important issue and for the complete solution, the seismic specialist shall necessarily find the soil response and present an appropriate zonation output to the civil engineer. For tackling this problem, a fuzzy clustering method has been applied in acquiring microtremor data more specifically in the city of Meybod. Dataset is divided into four subsets based on its intrinsic complexity by GK fuzzy clustering. Features in this classification practice are including the coordinates, the output dominant frequency of H/V method and the related amplitudes. Euclidian distance matrix norm is constructed to detect hyper ellipsoidal clusters with different orientations (shape and size) in the dataset. The cluster means are then refreshed in an iterative manner so as to identify the uniform seismic answer into the isolated gatherings. We used this method to determine the four divided seismicity regions with different range of frequencies. In addition, various type of soil structures in Meybod city with high and weak risky area have been cleared and can be applied in hazard and earthquake engineering projects. This approach was significantly well matched with clay and silt dominant on soil observed in the boreholes. Finally zonation maps based on this new method is provided. [ABSTRACT FROM AUTHOR]

Machine indexer is referred to as a kind of indexing system in which keywords are extracted from the title or body of a text and organized in the index entries using computer algorithm. Although there is an increasing need for applying computer in indexing machine indexer has not been developed for Persian language yet. Therefore in this paper the results of investigating and identifying of theoretical and practical aspects of such system in the Iranian Research Institute for Information Science and Technology are presented. To do so, firstly the relationship between indexing and information retrieval, theoretical debates and the machine indexing revolution, the practical and procedural viewpoints of machine indexing, successful projects in different countries and for different languages, and the requirements of machine indexer for Persian language are discussed. Then the elements of a machine indexer for Persian language are defined and its conceptual model and also the relationships between the elements, the details of subsystems, and the indexer logical system are designed. Finally, the requirements of developing the system for the Iranian Research Institute for Information Science and Technology are discussed and the state of the art of the implemented projects and existing experiences in this regard are defined. [ABSTRACT FROM AUTHOR]

Abstract: In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. The SVM method, a data-driven approach to pattern recognition, had a correct-classification rate of 52.38% for twenty-one boreholes divided into five classes. The results of the study indicated the capability of SVM as a supervised learning algorithm tool for the predictive mapping of mineral prospects. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk. [Copyright &y& Elsevier]

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R2), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS-ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS-ICA model with R2 of 0.942 can forecast PPV better than the USBM with R2 of 0.634, Ambraseys-Hendron with R2 of 0.638, Langefors-Kihlstrom with R2 of 0.637 and Indian Standard with R2 of 0.519. [ABSTRACT FROM AUTHOR]