جستجوی مقالات مرتبط با کلیدواژه "رگرسیون" در نشریات گروه "زمین شناسی"

Gully erosion is a destructive form of water erosion that cuse lost a significant amount of valuable soil and its control requires understanding the relationships between the characteristics that affect this phenomenon. The purpose of this study was determining the most effective variables on the gully volume and length.
Gullied area identifed with Google earth in the Jahan Abad, Buin zahra (Qazvin province), 33 gullies were selected, recorded by GPS and their morphologic characteristics including length, width and depth were measured. Surface soil samples were taken from headcut and were analyzed for texture, EC, pH, organic matter and some Anions and Cations. Indices related to the form of watershed were extracted from the digital elevation model which derived from UAV image processing. Correlation between factors, PCA and multivariate regression were performed to determine important and effective factors on gullies volume and length in MINITAB.
 Results indicated that gullies have vertical headcuts, trapezoidal cross-sections and linear general plan. The average depth of headcuts, depth at 50% of the gully length and the average gully length were 0.35, 0.47 and 13.46 m, respectively. Soil texture was loam and average soil texture components including sand, silt and clay were 38, 38 and 24%, respectively. The mean EC and pH of the samples was 11.2 dSm-1 and 8.7 respectively. Linear correlation between the total volume of soil volume and length and other characteristics showed that silt have significant correlation coefficients at 1% percent level 0.465 and 0.510, respectively. PCA with soil characteristics and watershed characteristics showed that the first and second components are close to 41% and 8 other dimensions with egen value more than one, with 84.2% are responsible for changes. Chlorine, electrical conductivity and sodium with negative effect and aggregate stability, canopy and amount of sand with positive charge in the first component have the most weight, while in the second component, sand, canopy and amount of gravel with positive effect and TNV, organic matter and saturation percentage play a more important role. The results of multivariate regression also showed that carbonate, magnesium, potassium, organic matter, saturation percentage, amount of sand, slope of gully floor, slope of inlet waterway to headcut, litter, basin primeter, respectively, effective properties on soil loss by gully in the region. Form coefficient had the greatest effect on soil loss, respectively, and were included in the prediction equation of soil loss with R2 of 0.837. Stepwise regression with the dependent variable of gully length showed that the relationship has R2 of 72.98% and is significant at the percentage level.
Factors affecting the length of the gully included the percentage of saturation, the amount of sand, specific gravity, the slope of the gully floor, the slope of the waterway entering the upstream, geravel, basin area and slope.

The North-Caspian Sea Pattern (NCP) is one of the atmospheric phenomena that originates from the pressure fluctuation at the 500 hectopascals level between the North Sea and the Caspian Sea. For the North-Caspian Sea Pattern, a numerical index has been defined, which is calculated based on the geopotential height difference of 500 hectopascals between the North Sea and the Caspian Sea. After the numerical calculation of this index, the output number is positive or negative, and the output of the positive number indicates the negative phase and the output of the negative number indicates the positive phase of this index.

To investigate the relationship between the precipitations of Ardabil province with the North-Caspian Sea Pattern, first the average precipitation, temperature and relative humidity of Ardabil, Parsabad, Khalkhal and Meshgin-Shahr stations during 1987-2015 was prepared from the IRI Meteorological Organization.Then, the data of the North-Caspian Sea Pattern for the period of 1987 to 2005 were prepared and used from the climate research system of East Anglia University. In order to calculate the mentioned index until 2015, statistical equations were used and the numerical amount of NCP index was prepared for the years 1987 to 2015.Then the monthly, seasonal and annual relationship of the North-Caspian sea pattern with the parameters of precipitation, temperature and relative humidity of Iran during a period of 29 years (1987-2015) was investigated with Pearson correlation at 95 and 99% level. In order to model the relationship between precipitation and the NCP model, the forward perceptron artificial neural network model was used.

The results showed that the correlation between monthly, seasonal and annual precipitation in Ardabil with the NCP index is not significant, and relative humidity has no significant relationship with the NCP index, but the temperature in February and July had a significant relationship with the NCP index at the 95% level.The results also showed that the relationship between precipitation and NCP index in Khalkhal is more than Ardabil, which was significant in November and December as well as summer and autumn seasons and the annual average of their relationship was significant at the 95 and 99% level. In November and December (autumn season), the correlation coefficient of precipitation and NCP index was positive, which shows that the amount of precipitation increases as the NCP index becomes positive. The correlation between the NCP index and the precipitation of Mashgin-shahr in January and December was significant and positive at the 95% level. The highest correlation between precipitation and NCP index was observed in Pars-Abad, especially in autumn, and at the 99% level, their correlation was significant and positive; this means that with the positive NCP index, the amount of autumn precipitation in the Moghan Plain will also increase. In order to fit the best artificial neural network model to the data, a regression line was used, and in this model, the R coefficient was 0.98 for the test data, 0.98 for the validation, and as overall, for the mentioned neural network model, its coefficient was equal to 0.98%, which indicates the appropriate fit of the model in predicting the amount of autumn precipitation in Pars Abad.

Moghan Plain, which is located in a flat area, and Sablan Mountains and high altitudes do not have an effect on its climate, are the only Tele-connection patterns that determine its precipitation regime, and this is the reason why the relationship between autumn rains is positive and significant with the NCP index. The positive phase of the North-Caspian sea pattern has been associated with the high Mediterranean trough, so that the studied area is located in front of the trough and the transfer of moisture from the Mediterranean Sea, the Atlantic Ocean and the Black Sea has caused autumn rains in Parsabad. Therefore, in spite of the influence of ENSO and North Atlantic Oscillation patterns on the precipitation of Ardabil province, the North-Caspian Sea pattern also plays an important role in its autumn precipitation, especially in the Moghan plain.

Previous studies have shown that moisture has a special effect on the static properties (uniaxial compressive strength (UCS) and elastic modulus (Es) of the rock. In this study, thin section, X-ray diffraction (XRD), porosity, UCS and Es, point load index, and Brazilian tensile strength of the limestone specimens were determined in Khersan 2 dam site, in south west of Iran. Then, using artificial neural network and simple regression, the effect of dry point load index, dry and saturated tensile strength, and porosity on UCS, Es were assessed. Microscopic studies of the samples showed that calcite is the main mineral and samples classified from the Mudstone to the Grainstone. The effect of water on the static properties showed that prediction models in dry conditions are more accurate. Calibration of the relationships presented by previous researchers based on the experimental results of this study and using the criteria of coefficient of determination and root mean square error (RMSE) showed that most of the relationships can be used to estimate the properties of Asmari limestone. Also, investigation of heteroscedasticity graphs of residual variance at predicted levels, determination coefficient and RMSE of the methods showed that the neural network has higher accuracy than simple regression. As compared to the regression method, the neural network is conservative in estimating these properties.

This research has investigated the possibility of predicting the direction and speed of Gusty wind by using the information of Mehrabad Airport runway automatic station during the period of January 2013 to June 2013, the Metar report of Mehrabad Synoptic Station in the period of 2013, and regression method.The data of the automatic station is taken from three sensors located in the band with a length of 4000 meters and a width of 45 meters, in southeast-northwest direction.The sensor number 29 and the sensor number 11, are at the northwestern end of the band and southeast edge of the band respectively. The Mid sensor location is at the middle of the band which distance from the band is 600 meters to the north direction.    First, all data (except the data of Gusty wind direction and speed measured by the sensors) was normalized to intervals 0.1-0.9. Second, all the data of sensors were randomly divided into three unequal parts: 70% of the data was stored for training, 50% of the remaining data was used for testing and the rest was used for validation. During the calculations, they were used instead of the original data. Third, the quantities were processed by using the three methods of feature selection: Sequential Forward Feature Selection(SFS); Backward(SBS) and Mutual Information(MI) with the method of the Maximum-Relevance and Minimum-Redundancy criterion. At this stage, selective features by every method were separately used in the linear regression method to predict the speed and direction of Gusty wind in the winter and spring seasons. The results were then compared with each other.The results show that the selected features by SBS method for wind speed in winter are similar to spring, but their wind direction is slightly different. Selected features for winter Gusty wind with SFS method are a subset of the set of the selected features for spring. Selected features with MI are similar for the two seasons but with different weights. The performance of the selected features for wind speed are better than for wind direction. The SFS method is optimal for selecting features of Gusty wind in the Mid runway. On runway 11, the SBS method and the SFS are optimized for predicting the Gusty wind speed and direction respectively. On runway 29, the SBF method is very suitable for selecting features related to Gusty wind speed and direction.Finally, by examining the output of the models for each of the runways, an equation is provided to predict the direction and speed of the Gusty wind in each runway.The quantity of predicted wind direction in runway 29 and 11 depends on the mean wind direction in 2 minutes, the minimum and mean direction in 10 minutes and the wind speed component along the runway, but the quantity of predicted wind speed depends on the maximum wind speed in 10 minutes on the runway, the instantaneous pressure of the station, and the pressure of the station relative to sea level.  The quantity of predicted wind direction in Mid runway depends on the minimum and mean wind direction in 2 minutes, minimum, mean and maximum wind direction in 10 minutes in runway Mid, maximum wind speed in 2 minutes and components of wind direction along with the runway 11. The quantity of predicted wind speed depends on the maximum and minimum wind speed in 10 minutes on the runway, deviation of wind direction during the last 10 minutes in the runways 29, Mid and 11.

Abstract Conventional methods for determining the geotechnical properties of the soil are usually costly, destructive and time-consuming. Electrical resistivity is one of the subsurface characteristics of soils that can easily be measured without any invasive action. It is very useful to develop an acceptable correlation between electrical resistivity and shear strength parameters in construction sites. In this study, a correlation was developed between the electrical resistivity and each of the parameters of cohesion and internal angle of friction, with laboratory measurements. Based on the regression analysis, there is a relatively strong correlation with the coefficient of 0.7769 between the electrical resistivity and the cohesion parameter. Regression analysis also shows a relatively good correlation between the electrical resistivity and the internal angle of friction parameter. The results showed that the electrical resistivity relationship with each of parameters of cohesion and internal angle of friction is linear .

Uniaxial compressive strength (UCS), modulus of elasticity (E) and Brazilian tensile strength (BTS) are important properties of intact rock which are used widely in rock engineering. The determination of these parameters in laboratory is difficult, expensive and time consuming. Thus, it has been attempted that the empirical equations to estimate the mechanical properties of sandstones using P-wave velocity and Schmidt hardness as two non-destructive and easy tests were developed. For this purpose, 30 rock blocks of sandstone have been collected from several parts of Upper red formation in southwestern Qom and they were tested in the laboratory. The laboratory tests include the determination of P-wave velocity and Schmidt hardness, and mechanical properties include the uniaxial compressive strength, modulus of elasticity and Brazilian tensile strength. Empirical equations have been developed for estimating the mechanical properties from P-wave velocity and Schmidt hardness using regression analysis. To check the validity of the empirical equations, t and F test were performed, which confirmed the validity of the proposed empirical equations. Moreover, the results show that P-wave velocity appears to be more reliable than the Schmidt hardness for estimating the mechanical properties.

The prediction of the minimum temperature is one of the main approaches to manage and reduce the risk damage caused by frostbite. For this purpose, using daily statistics of minimum, maximum and dew point temperatures during the period of 2005 to 2009, the minimum temperature of the next day was predicted for four stations (Rasht, Kerman, Shiraz and Hamedan) with different climate types by applying the Linacre regression model and the Multi-Layer Perceptron artificial neural network (MLP) in the whole year, the cold period (from October to late March) and the warm period (from April to late September). For this aim, the Matlab-2015 and IBM SPSS-20 software were used and statistical criteria RMSE, NRMSE and R2 were applied to evaluate the performance of the models. The results of this study, in all three periods, demonstrated that the best estimate of the Linacre regression model was obtained with root mean square error of 1.70 and 2.44°C for the whole year, 2.01 and 2.32°C for the cold period and 1.51 and 2.24°C for the warm period for Rasht and Shiraz stations with the PH-C-W and SA-C-W, respectively. The best results from MLP neural networks with Levenberg-Marquardt algorithm, logic sigmoid transfer function in the hidden layer, the linear transfer function in the output layer and two inputs (dew point and maximum temperature), like the Linacre regression model, were obtained with RMSE of 1.57 and 1.93°C for the whole year and 1.61 and 1.8 for the cold period for Rasht and Shiraz stations, respectively. The RMSE of the best results from MLP neural networks in the warm period was 1.21 and 1.44°C for Rasht and Hamedan stations, respectively. To evaluate the role of relative humidity on model results, this parameter was added as a third input to the multi-layer Perceptron network. The improved RMSE for the whole year was 17.4, 12.9, 49.4 and 18.3 percent and for the cold period of the year was 3.4, 2.86, 9.48 and 15.83 percent at Kerman, Shiraz, Hamedan and Rasht stations, respectively. These values for the warm period were 6, 13.33, 2.86 and 18.63 percent for the above mentioned stations. These improved errors indicate that only at Hamedan station, the cold period of the year produces more improvement in error reduction than the warm period of the year by adding relative humidity as the third input to the neural network model. In other stations, adding relative humidity in the warm year has led to a reduction in the error rate. In general, it can be said that selected MLP networks had better performance than the Linacre regression model in predicting the minimum daily temperature.

In the present survey، it has been tried to demonstrate a significant correlation between air pollutant concentrations and meteorological parameters، by the study of the atmospheric conditions in an air pollution episode. Carbon monoxide and nitrogen dioxide were selected as air pollutants. Temperature، wind speed and direction and the dimensionless Richardson number were considered as the meteorological parameters in this study. To this aim، a period of eleven days from 30 November to 10 December 2012 was considered. In this period، a severe temperature inversion and consequently a long-term stable situation was seen in the Tehran weather. The governing stability classes were studied using the Turner algorithm and vertical temperature gradient scheme. For the mentioned period، the sun altitude، wind speed at the height of 10 meters، total cloud cover and ceiling were extracted from the U. S. Naval Observatory web page (http: //aa. usno. navy. mil/data/docs/AltAz. php) to be used in the Turner algorithm. The stability analysis showed that for this time interval، stable conditions prevailed in 89% of cases. The sounding data was collected from the Mehrabad weather station. By the sounding data، the daily maximum mixing depth was calculated and the vertical trend of the temperature was plotted in each day during this period. The morning vertical temperature gradients showed a strong temperature inversion in these eleven days. Also، the maximum mixing depth decreased to the minimum value of 600 meters on the 6th day of the time interval. The maximum mixing height and visibility graphs also confirmed that the atmospheric mixing decreased and consequently more pollutants were trapped in the middle of the period. Once confirming critical conditions by comparing the stability classes، maximum mixing depth and the visibility، the correlation between atmospheric parameters and pollutants concentration was obtained by multiple regression method. Among the meteorological parameters that were considered، temperature، wind speed، wind direction and the Richardson number had a greater correlation with the pollutant concentration. The bulk Richardson number was calculated from the sounding data and used in the regression. The CO and NO2 concentrations were gathered from the pollutant concentration measuring station which is located on the region-10 municipality building، 2500 meters far from the Mehrabad weather station. The coefficients of correlation between the normalized CO concentrations and temperature، West-East component of wind، South-North component of wind and the Richardson number were 0. 707، 0. 078، 0. 028 and -0. 019، respectively. And for NO2 concentration، the correlation coefficients were 0. 353، 0. 016، 0. 015 and -0. 019، respectively. It could be observed that the coefficients of temperature and surface wind for the CO concentration were almost twice the corresponding coefficients for the NO2 concentration. The reason is that the NO2 concentration has been influenced by chemical and photochemical reactions and the mixing depth، while the CO concentration is only affected mainly by the rate of transportation and the mixing depth. In fact، the CO concentration had a stronger functionality to temperature and wind speed than the NO2 concentration. In the most cases، the Richardson number had a positive value and by increasing its value (in the early morning hours or night hours)، the pollutant concentration were reduced. Therefore، as expected، its coefficient were negative in both regressions.