, Greza Khanlari, M Heidari, Yazdan Mohebi, Reza Babazadeh,
Volume 7, Issue 2 (3-2014)
Abstract
Awareness of orphological features of rivers is necessary for recognition of river behavior and optimum application of rivers. Overall catchment physiografy have important role for determination factors such as floods, erodible and sediment mutagenicity. In this study in order to understand the behavior of Gamasiab River in the east of Kermanshah province, geomorphologic features of this river has been considered. Study of engineering geomorphologic properties is done by using existing data from previous studies, site visit and field perceptions, study of geology and topography maps. Physiographic properties of catchment, channels morphologic properties and geology conditions in this region have been studied. In this research, several parameters such as average width, environment, area, hydrogeologic coefficient, catchment form, maximum, minimum and mean high, and longitudinal slope has calculated. Also status of drainage density of this river has been investigated and time to focus calculated. Finally this river review and classified according to various classifications for rivers
Mr. Farhad Mollaei, Dr. Reza Mohebian, Dr. Ali Moradzadeh,
Volume 18, Issue 3 (Autumn 2024)
Abstract
The brittlenessindex is one of the most important parameters in geomechanical analysis and modeling. Many methods have been proposed to estimate the brittleness index. One of the recently used methods is the intelligent method. In this paper, firstly the aim is to introduce a new algorithm using deep learning algorithms to predict the brittleness index in one of the wells of the hydrocarbon field in southwest Iran. In this article, first, the effective features for the input of the algorithms were determined using Pearson's correlation coefficient, and then using (recurrent neural network + multi-layer perceptron neural network) (LSTM + MLP) and (convolutional neural network + recurrent neural network) (CNN+ LSTM) brittleness index was estimated and the mean error value (MSE) and coefficient of determination (R2) were calculated for the training and test data. For both training and test data, both algorithms have a coefficient of determination close to 1 and a very low error. Also, in order to ensure the results of the algorithms, a part of the data was set aside as blind data, and the error and coefficient of determination were calculated for this data, and the error was MSE CNN+LSTM =26.0425, MSE LSTM+MLP =32.0751 and the coefficient of determination was R2 CNN+LSTM =0.8064, R2 LSTM+MLP =0.7615 . The results show the effectiveness of the introduced deep learning algorithms as a new method in predicting the brittleness index, and comparing the two algorithms presented, the CNN+LSTM algorithm has higher accuracy and less error.
