List of Articles Linear regression

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  1 - Estimating the LNAPL level elevation in oil-contaminated aquifer by using of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)
  فاطمه  ابراهیمی Mohammad Nakhaei HamidReza Nasseri  
  One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maxi More

  One of the main concerns in the aquifers adjacent to oil facilities is the leakage of LNAPLs. Since remediation processes costly and time consuming, so the first step in these systems is determining design goals. Often the most important goal of these systems is to maximize pollutant removal and minimize the cost. Identifying the thickness of LNAPL and its fluctuations can determine the type of recovery method and thus can be effective on the amount of removal and the cost of the implementation. In this study, three methods of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and multivariate linear regression (MLR) were used to estimate and predict the LNAPL level. Input variables are groundwater level elevation and discharge rate of LNAPL and the output variable is the LNAPL level elevation. The results of the three models were analyzed by statistical parameters and it was determined that GEP technique has better results and could be used successfully in predicting LNAPL level fluctuations in recovery processes. Also, the GEP model provides an equation for predicting the LNAPL level that can be used in the field to predict the elevation of the LNAPL level. Manuscript profile
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  2 - Development of Multivariate Regression Relationship Between Factors Affecting Unemployment Rate
  roya soltani mahnaz ebrahimi sadrabadi Ali Mohammad Kimiagari
  In this research, the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394, which is compiled from reliable economic data bases of country. Independent variable More

  In this research, the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394, which is compiled from reliable economic data bases of country. Independent variables are: net foreign assets of the banking system (billion rials), net debt of the public sector to the banking system (billion rials), liquidity in terms of its constituent parts (billion rials), rate of dollar (rials), economic participation rate, average inflation rate, The average annual interest rate on state-owned banks, the percentage of jobseekers (65-15). The results indicate that there is a negative and significant relationship between unemployment rate and average inflation rate and economic participation rate, while the net debt of the public sector has had a positive and significant relationship with the banking system and unemployment rate. The greatest negative effects on unemployment rate are the rate of economic participation and the greatest positive impact on the unemployment rate is the net debt of the public sector to the banking system. Manuscript profile
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  3 - Evaluation of trend of rainfall and temperature changes and their effects on meteorological drought in Kermanshah province
  Maryam Teymouri Yeganeh Liela Teymouri Yeganeh
  Climate change is one of the natural features of the atmospheric cycle, which results in anomalies or fluctuations in the process of meteorological parameters such as rainfall and temperature. Also, drought is one of the weather and climate disasters, including catastro More

  Climate change is one of the natural features of the atmospheric cycle, which results in anomalies or fluctuations in the process of meteorological parameters such as rainfall and temperature. Also, drought is one of the weather and climate disasters, including catastrophic events. It alternates with floods and causes significant damage each year. Lack of rainfall has different effects on groundwater, soil moisture and river flow. For this reason, the study of changes in precipitation and temperature has always been the focus of researchers in various sciences, including natural resources and the environment. In this study, using the data of Kermanshah Meteorological Organization related to 30 years of rainfall, average minimum temperature and average maximum temperature in three stations of Kermanshah, Islamabad West and Sarpol-e Zahab to assess the severity of drought each year by DIC software Using standard precipitation index (SPI) and examining the trend of temperature changes using two non-parametric Mann-Kendall tests, Sensitimator and also linear regression. In order to study the drought trend during the 30-year period, statistical software was used and the results showed that during the 30-year period, all three stations are in near normal condition. Also, the results of temperature changes using the mentioned tests indicate the increasing trend of temperature and this trend is significant at the level of 99% using two non-parametric Mann-Kendall tests. Manuscript profile
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  4 - Porosity modeling in Azadegan oil field: a comparative study of Bayesian theory of data fusion, multi layer neural network, and multiple linear regression techniques
  عطیه  مظاهری طرئی حسین معماریان بهزاد تخم چی بهزاد مشیری
  Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for More

  Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using these techniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%. Manuscript profile