جستجوی مقالات مرتبط با کلیدواژه « برنامه ریزی بیان ژن » در نشریات گروه « اکولوژی »

Groundwater is an important source of exploitation in arid and semi-arid regions. For this reason, in order to maintain groundwater quality and its optimal management, it is important to know their spatial and temporal distribution and their monitoring and zoning should be considered as an important principle in the country's water resources planning. The aim of this study was to zoning the electrical conductivity of groundwater in the Mashhad plain aquifer using 5 methods of distance inverse geostatistics (IDW), local estimator (GPI), general estimator (LPI), kriging and cokriging and also evaluating the gene expression programming model in predicting this The parameter is using spatial data. For the present study, data from 122 observation wells in the aquifer area of Mashhad plain were used. To compare the methods used, two squares evaluation criteria were the mean squared error (RMSE) and the mean absolute error (MAE). The semi-variable plot in GS + showed that the electrical conductivity data fit best in the spherical model. The results of the present study showed that among the mentioned methods, the inverse distance method (IDW) with error rate RMSE=0.3 mos/cm and MAE=0.16 mos/cm and then the model of gene expression programming with RMSE=275.54 mos/cm MAE=223.15 mos/cm with the highest accuracy and local estimator method (GPI) with RMSE=996.11 mos/cm and MAE=755.56 mos/cm, had the least accuracy in this field.

Streamflow forecasting is necessary for water resources management and planning in rivers, lakes, reservoirs and protection of river banks during flood time. In this study, different soft computing models including artificial neural networks (ANN), the hybrid of wavelet-artificial neural networks (WANN), gene expression programming (GEP) and least square-support vector machines (LS-SVM) was utilized for river flow estimation of Khiav-Chay. Statistical measures and ANOVA test was used for evaluation of applied models. The results indicated that WANN model was the best one with the highest correlation coefficient (R=0.877) and lowest root mean squared error (RMSE=0.696) and Nash Sutcliff coefficient (NS=0.767) in validation phase. The results of ANOVA test was in agreement with statistical criteria values and WANN model with the lowest F statistic (F=0.11) and highest significant resultant (0.75) was selected as the best model. Furthermore, in estimation of maximum discharge, WANN with mean relative error equals to 30.19% has the minimum error of estimation compared to other models.

Precipitation is considered as one of the most important factures in water cycle. Prediction of monthly Precipitation is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins.In this study we studied and evaluated gene expression programming to predict the Precipitation of the Kakareza river (in lorestan), and the results were compared with results of anfis and artificial neural network model. For this purpose, mean temperature, relative humidity, evaporation, wind speed rate at monthly scale during the period (2005-2015) as input and output parameters were selected as Precipitation . The criteria of correlation coefficient, root mean square error and of mean absolute error were used to evaluate and performance compare of models. The results showed that gene expression programming model has the highest correlation coefficient (0.978), the lowest root mean square error (0.026 mm) and the lowest mean absolute error (0.017mm) became a priority in the validation phase. The results showed that the gene expression programming model to estimate high minimum and maximum values of Precipitation .

In this study, climatic parameters are used to evaluate the sensitivity of reference evapotranspiration in Isfahan and Rasht with two semi-arid and moderate climates using two intelligent methods during 1970-2010. In this research, multi-layer precpetron e (MLP) and gene expression programming (GEP) intelligent models are used in order to analyze the sensitivity of reference evapotranspiration to meteorological parameters. Results of correlation of the parameters showed, in Isfahan and Rasht, maximum and minimum temperatures have the highest correlation with correlation coefficients of 0.968 and 0.926 with other parameters, and in Isfahan wind speed and maximum temperature with a coefficient of 0.067 and in Rasht, minimum humidity and wind speed with a coefficient of 0.001 have lowest correlations. The results of the survey indicate that in Isfahan using MLP model and the maximum and minimum temperature, humidity, wind speed and number of sunshine hours as inputs, with the root mean square error (RMSE) equal to 0.418 mm/day was the best results for estimating reference evapotranspiration. The application of MLP model in Rasht, with the inputs of maximum and minimum temperature, the maximum humidity and the number of sunshine hours has less error and higher correlation coefficient and the values of reference evapotranspiration are much more similar to reality. Also, about the evapotranspiration of reference plant in relation to parameters that are used in this study the results showed that in two study places, the correlation coefficient between wind speed and reference evapotranspiration are 0.3 and 0.061 in Isfahan and Rasht, respectively which are the lowest coefficients.