Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. There are a lot of well log data related with this parameter. In this study, permeability is predicted using them and a supervised COMMITTEE MACHINE neural network (SCMNN) which is combined of 30 estimators. All of data were divided in two low and high permeability populations using statistical study. Each estimator of SCMNN was combined of two simple networks to predict permeability in both low and high classes and one gating network, considered as a classifier, classified data to these two classes. Thus, each low and/or high input data would predict in related network. This SCMNN was used to predict permeability on the data of one of petroleum reservoirs of south-west of Iran. 210 samples of this reservoir were available. Because of the fewness of data 80% of them were used as training data and 20% of them were used as validation and testing data. The overall fitting between predicted permeability versus measured ones was qualified through R2 (R=correlation coefficient) to be 97.72% which is considered appropriate. Whereas, R2 in the simple network in the best stat was 84.14%. The high power and efficiency of SCMNN are indicated by lower bias and better R2 in results.

In recent years, declining the water level of Lake Urmia has caused water and environmental crisis in the area. Therefore, it is urgent to carry out an accurate and reliable management and planning which requires modeling the lake's water level for the future. In this research, the artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector MACHINE (SVM) models were used to forecast the Lake Urmia water level fluctuations for one, two and three months ahead forecast horizons and finally, a supervised COMMITTEE MACHINE artificial intelligence (SCMAI) model was used to obtain a better performance than the used individual models. To develop the models, the current month [h (t)] and eleven months water level lags [h (t-1), h (t-11)] were introduced as input variables to forecast one, two and three steps ahead water levels. The datasets were divided into two subsets of training/validation (90%) and testing (10%). The performances of the models were evaluated based on the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE). The results showed that the SVM models had better performance than the ANN and ANFIS models. The SCMAI model was applied to combine the used models’ outputs and illustrated that the SCMAI models are able to improve the performance of the individual artificial intelligence models. The results of the performance criteria for SCMAI model indicated that the one-month step ahead water level modeling with R2, RMSE and MAE equal to 0. 9896, 0. 0547 m and 0. 0421 m, respectively outperformed in comparison with SVM model which this performance is reliable for the two-and three-months step ahead lake's water level.

The proper prediction of water level variation in dam reservoirs is considered as one of the important issues for design and operation of dams and water supply management. In this study, based on five soft models such as support vector regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN) and the combined use of their results as input to one of these five models, a new structure called supervised intelligent COMMITTEE MACHINE (SICM) was proposed to predict the monthly reservoir water level of Karaj Amirkabir dam. The data used in this paper are water level, precipitation, evaporation, and inflow to and outflow from the dam. The evaluation of these models was done by nine error indexes and also the best model among all was selected using Vikor decision maker method. Evaluations showed that among the used soft models, the ANN was the best model with Nash– Sutcliffe efficiency (NS) and mean square error (MSE) equal to 0. 89 and 23. 37 square meters, respectively. The results of the proposed approach showed that the supervised (hybrid) neural network (SICM-ANN) has been able to provide high performance in predicting the monthly reservoir water level in Karaj dam with increasing the NS coefficient to 0. 94 and decreasing the MSE index to 12. 85 square meters (more than 45 percent decrease). Accordingly, hybrid use of soft models can effectively be applied for a significant reduction in the predicted error of water level compared to single models.

In this study we propose a new approach to analyze data from the P300 speller paradigm using the quadratic B-Spline wavelet coefficients in comparing to time and frequency features sets on the event related potentials. Data set II from the BCI competition 2005 was used. Mode frequency, Mean frequency, Median frequency and some morphologic parameters ware extracted as features. Three methods were used for comparing three feature subsets, first Davies Bouldin criteria, correlation based method' and classification accuracy criteria. For all criteria, best result was extracted from wavelet coefficients, at the final wavelet coefficients were used as inputs into COMMITTEE MACHINEs (CM) based on LDA, MLP and SVM. This algorithm achieved an accuracy of 97.6% for train data and 94.2% for test data of subject A in target and non-target detection also accuracy of 98.2% for train data and 92.8% for test data of subject B.

Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary to manage the groundwater resources by identifying areas with high potential of contamination. In this study, groundwater vulnerability in Ardabil plain aquifer was evaluated by applying DRASTIC model. DRASTIC model was prepared by seven effective parameters on vulnerability, including groundwater depth, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. These parameters were prepared as seven raster layers, and DRASTIC index was then calculated after ranking and weighting. The DRASTIC index value was obtained between 82 to 151 for the Ardabil plain. The main problem of this model is the subjectivity in determining rates and weights of the parameters. Therefore, the purpose of this study is to improve DRASTIC model using 5 methods of artificial intelligence (AI), such as Feedforward network (FFN), Recurrent neural network (RNN), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and COMMITTEE MACHINE (CM) to obtain the most accurate results of vulnerability evaluation. Because of heterogeneity in the Ardabil Plain, it is divided into 3 sections including west, east and north, and each section needs an individual model. For this purpose, the DRASTIC parameters and the vulnerability index were defined as inputs data and output data respectively for models, and nitrate concentration data were divided into two categories for training and test steps. The output of model in training step was corrected by the related nitrate concentration, and after model training, the output of model in test step was verified by the nitrate concentration. The results show that all of the artificial intelligence methods are able to improve the DRASTIC model, but the supervised COMMITTEE MACHINE artificial intelligence (SCMAI) model had the best results. According to this model, the most of high pollution potential areas located in western and northern parts of the plain, and need more protection.

Permeability is the ability of a porous rock to transmit fluids and is one of the most important properties of a reservoir rock because the oil production depends on reservoirs permeability. Permeability is determined using a variety of methods that are usually expensive and time consuming. Analyzing the properties of a reservoir rock with image analysis and intelligent systems saves time and money. This study presents an improved model based on the integration of petrographic data and intelligent systems to predict the permeability. Petrographic image analysis was employed to measure the types of porosity including intergranular, intragranular, moldic, micro and optical, as well as the amount of cement, limestone, dolomite and anhydrite, the types of texture and the mean geometrical shape coefficient of pores. Permeability was first predicted using the three individual intelligent systems including neural network (NN), fuzzy logic (FL), and neuro-fuzzy (NF) models, respectively. The mean squared error (MSE) of the NN, FL and NF methods are 0. 0107, 0. 0081 and 0. 0080, which correspond with R2 values of 0. 8830, 0. 9193 and 0. 9136, respectively. Afterward, two types of COMMITTEE MACHINEs were used with intelligent systems (CMIS) to combine the predicted values of permeability from individual intelligent systems: simple averaging (SA) and weighted averaging (WA). In the WA, a particle swarm optimization (PSO) was employed to obtain the optimal contribution of each intelligent system. The MSE of the CMIS-SA and CMIS-WA were 0. 0072 and 0. 0066, which correspond with R2 values of 0. 9262 and 0. 9260, respectively. These show that the CMIS-WA performed better than the NN, FL, and NF models individually. In addition, a multiple linear regression (MLR) was used to compare the results with the other techniques. The R2 value between the core and MLR permeability is 0. 8699. Therefore, the integration of petrographic data and intelligent systems provided more accurate results than the MLR model.

Stoneley wave velocity (Vst) which is a type of surface waves provides valuable information from hydrocarbon reservoirs. In this study Vst was predicted from well log data using neuro-fuzzy, Sugeno and Mamdani fuzzy inference systems, and then results of each intelligent system were combined by an intelligent COMMITTEE MACHINE (CMIS). A genetic algorithm was used for constructing CMIS. For this purpose a total of 3030 data points from two wells in Sarvak carbonate reservoir which have Vst and conventional well log data were utilized. These data were divided into two parts, one part included 2047 data points used for constructing intelligent models and the other part included 983 data points used for models testing.The results show that CMIS technique has been useful method for prediction of Vst in Sarvak Formation with correlation coefficient equals to 0.98 and mean squared error equals to 0.000096.

The inherent duty of urban design is to manage the improvement of urban design qualities in both content and procedural dimensions. Without simultaneous attention to these two, even the purest urban design theories may remain ineffective in reality. Improving the quality of the landscape, in addition to the need to formulate comprehensive, clear, and more or less flexible regulations, requires the definition of a precise and efficient executive structure so that it can be a suitable leverage for the implementation of these rules. Façade COMMITTEEs are one of the methods regarded for the control and implementation of these approvals, which play a significant role in achieving the goal of landscape improvement. Mashhad City has been one of the main pioneers in this field in the country by compiling approvals and forming COMMITTEEs to control and guide urban landscaping. The present research aims to evaluate the performance of the Mashhad façade COMMITTEE by examining its damages, challenges, and achievements as well as providing effective suggestions to improve this performance. Applying a mixed method and alignment methodology, this study has evaluated the performance of the façade COMMITTEE in two quantitative and qualitative phases. The research findings state that the most important challenges of façade COMMITTEEs are qualitative decisions, applying personal interest, weakness of legal position, and lack of criteria for action and executive guarantee. It's most important achievements are drawing the attention of the specialized community to the significance of the public landscape, citizens' rights and interests, and strengthening the architects' position in the development process. Some suggestions have been proposed for improving the performance of the façade COMMITTEE in the procedural section that includes improving the organizational administrative level of the façade COMMITTEE, forming a project steering COMMITTEE, preparing periodical performance reports. Some other suggestions were proposed in the case of the content section, including planning for participation and opinion polls, and acculturalization for Iranian architecture promotion.