linear regression

Air pollution has become one of the major challenges associated with urbanization and industrialization. This study aims to conduct a spatial analysis of PM2.5 pollution and examine its statistical relationship with meteorological parameters in the metropolitan area of Tehran. To achieve these objectives, two data sets were utilized: (1) daily PM2.5 concentration data obtained from the Air Quality Control Company and (2) daily meteorological data (including rainfall, evaporation, maximum wind speed, minimum and maximum relative humidity, sunshine hours, and minimum and maximum temperatures) provided by the Meteorological Organization. The inverse distance weighting (IDW) method was used to map the spatial distribution of PM2.5 concentrations. The IDW results revealed that regions 16, 19, 20, and 9 had the highest levels of particulate matter pollution, while regions 8, 22, and 15 had the lowest concentrations. Pearson correlation and multiple linear regression analyses were used to investigate the statistical relationship between PM2.5 levels and meteorological parameters. The Pearson correlation results indicated a positive correlation between PM2.5 and minimum/maximum temperature, as well as sunshine hours, and a negative correlation with maximum wind speed and rainfall. Two linear regression models (Enter and Stepwise) were employed to establish the relationship between PM2.5 (dependent variable) and meteorological parameters (independent variables). The results showed regression coefficients of 0.6 for the Enter model and 0.565 for the Stepwise model, indicating that both models performed well in predicting PM2.5 concentrations. To determine the more suitable model, the estimated standard error was compared, with the Stepwise model showing a lower error (5.84) than the Enter model (6.0089). Therefore, the Stepwise model was deemed more appropriate for predicting PM2.5 concentrations.

Continuous monitoring of temperature in extreme areas, such as mountainous and desert regions, is hampered with insufficient or sparse distribution of meteorological stations, as well as the complexity of interpolating existing station data. For this reason, the use of satellite remote sensing data has increased significantly in recent years. In this study, the daily LST data (daytime and nighttime) obtained from the MODIS Aqua satellite in the period from 2003 to 2019 at 1-km resolution, as well as the meteorological data of 11 synoptic stations, were used to estimate the air temperature in Yazd province. The assessment of the relationship between the mean monthly LST (day/night) and the mean monthly air temperature (maximum/minimum) indicated a strong correlation between them. The coefficient of determination (R2) values between LST daytime and maximum air temperature varied from 0.989 to 0.997 and between LST nighttime and minimum air temperature from 0.991 to 0.999. Therefore, according to the suitable spatial coverage of MODIS LST, it is possible to estimate the air temperature for different cells of Yazd province using the linear regression equation. In addition, the results indicated that the RMSE values at night were much smaller than the values during the day. Therefore, it is possible to retrieve the land surface temperature at night with a much higher accuracy than during the day. The findings of this research also showed that the estimated air temperature and LST have similar seasonal cycles. Although the difference between these two variables is greater during the day than at night. These differences between the air temperature and LST increase in the summer season (June to August).

In this article, the energy gap of 300 nitride compounds was analyzed and investigated using quantum simulations based on Density Functional Theory (DFT). The energy gaps of the compounds were calculated using two approximations, GGA-PBE and HSE06. The parameters considered in the machine learning studies were categorized into two groups: atomic and crystalline parameters. The atomic parameters include covalent radius, electronegativity, the number of valence electrons, and the first ionization energy. After collecting data on atomic features, a multiple linear regression model was fitted to the data. Subsequently, variable selection was performed using the stepwise regression method with the AICc criterion, and the effect size of various features was calculated. Additionally, to improve the accuracy of the model in predicting HSE06, three crystal features were incorporated into the model, and eight different models were fitted, each including one or more of these crystal features. The findings indicate that the model without any crystal variables has an adjusted coefficient of determination (R²) of 75.45%. However, with the inclusion of crystal features, the results improve significantly. Specifically, adding the PBE energy gap as a variable increases the R² to 99.03% (from 75.45% to 99.03%).

Dust is a common phenomenon in arid and semi-arid regions. One of the factorsthat play a role in the occurrence of this phenomenon is the change in weather conditions, whichleads to drought, and the drying of lakes and rivers has caused crises such as sandstorms. Thisphenomenon leads to negative effects on agriculture, water and soil pollution, and respiratorydiseases, as well as environmental and social challenges such as reduced visibility and roadaccidents. The purpose of this research is to investigate the relationship between the DSI dustindex and SPI standardized precipitation drought index in Semnan Province.

The studied area of Semnan Province is located in the southern regionof the Alborz Mountain range. To investigate the effect of drought on the amount of dust, thedaily data of dust, monthly rainfall, temperature, relative humidity and evapotranspiration offive synoptic stations of Semnan Province within a period of 15 years (2003-2017) were usedon an annual scale. During a 15-year period, the drought storm index (DSI) was determined forthe synoptic stations using the dust storm index, and then the cumulative DSI index wascalculated. Afterwards, statistical and experimental evaluations was done on annualfluctuations to identify potential trends. Also, experiment and linear regression analysis wereused to evaluate the correlation of standard precipitation index and dust storms, and Arc GISsoftware was used for zoning.

The results showed that the average annual temperature in the regionhas increased from 2003 to 2017. This rapid warming has caused drought and as a result,temperature, evaporation and transpiration have also increased and led to a decrease in rainfall.A month-by-month survey of wind speed in different stations showed that there is a possibilityof dust in the province in the months of June and July when the maximum wind speed prevails.On the contrary, the months of December and January experienced the least occurrence of dust.The annual changes of DSI (2003 to 2017) showed an irregular shape and do not have a trend.Thus, in 2011, the intensity of dust was high and decreased in the following year, and the samewas observed in 2016. According to the SPI index, Semnan Province was in the normal droughtcategory with a score of 0.59. The in-depth analysis of the correlation chart between thestandardized rainfall indices and DSI showed that the DSI has increased along with the severityof drought during the study period and there is a good correlation between the DSI index andthe meteorological drought in the region, so that when there was a drought period, the value ofDSI decreased and when it was a drought period, the value of the dust index increased.However, there is no significant relationship between these two indicators (p =0.07 andR2=0.22) during the 15-year research period.

Our findings indicated an increase in dust from the west to the east of the provincedue to the increase in average dust storm days. Correlation results between SPI and DSI indexshowed that although DSI index increased during the analyzed period along with the severityof drought, the correlation between the two was not significant. However, the DSI index trendpattern was consistent with the drought pattern trend. Finally, the correlation between droughtand DSI has always fluctuated according to droughts.

Biodiversity is an important structural feature of dynamic and complex forest ecosystems. One of the most challenging and important issues in assessing the structure of forest ecosystems is understanding the relationship between biodiversity and environmental factors. Hyrcanian Forests are considered a biodiversity hotspot in the world and have special and unique features that have led to an emphasis and importance of biodiversity conservation in these forests. The aim of this study was to investigate the effect of biotic and abiotic factors on the diversity and richness of tree species in Hyrcanian Forests from the west of Gilan province to the east of Golestan province. For this purpose, using 655 fixed sample plots (0.1 hectare), the diversity of trees in 3 provinces in the northern Iran from east to west of the Caspian Sea was analyzed. A combination of non-parametric models including random forest (RF) and support vector machine (SVM) and linear regression models were used to investigate the relationship between tree diversity and biotic and abiotic factors. Biotic and abiotic variables included the number of trees per hectare, diameter, respectively. Basal area (BA), Basal Area in Largest tree (BAL), slope, aspect and elevation. Evaluation statistics including the coefficient of determination, RMSE and percentage RMSE error showed that the random forest model was the best model to determine the relationship between biodiversity and environmental factors and has suitable accuracy for determining biodiversity changes in the northern forests of Iran.

Relationship between agricultural use area and fertilizer consumed with changes in groundwater nitrate concentrations is one of the direct methods of agricultural use on nitrate concentration and groundwater inflammation in each region that can manage managers in proper management. Help ground water resources. The purpose of this study was to investigate the relationship between changes in agricultural and chemical area of consumed on the increase in nitrate concentrations in groundwater sources in Ray plain.

In this study, Landsat 5 and 8 satellite images for the years 2008, 2011, 2014, 2017 and 2020 were used to monitor land use changes. After processing the images, the area of agricultural land was calculated. According to the statistics obtained from the amount of chemical fertilizer consumed and the qualitative laboratory results of nitrate concentration changes, the effects of these factors on each other and the mathematical relationships between the variables were tested with the Pearson correlation and linear regression method.

The results show that during the investigated period, agricultural land has increased and barren land has decreased. The consumption of chemical fertilizers has also increased. The level of nitrate concentration of underground water sources shows that during the period under review, the concentration of nitrate in underground water wells has increased. The analysis of the correlation coefficient also showed that at the error level of 1% (99% confidence level) there is a significant relationship between the concentration of nitrates in underground water with the area of agricultural land and the amount of fertilizer used. This means that as the area of agricultural use and the amount of chemical fertilizers increase, the concentration of nitrates in underground water resources also increases. In the presented regression model, the accepted square value (R2) is equal to 0.993 and it indicates a relatively strong relationship between the dependent variable (water nitrate concentration in underground water sources) and the independent variables (agricultural area and the amount of chemical fertilizer used).

The results indicate that with the increase in the area of agricultural land, the consumption of chemical fertilizers has increased in the research area, and due to the nitrate leaching process from the soil surface, the nitrate concentration in underground water sources has increased, which is one of the important reasons for the increase in nitrate concentration in the study wells.

Data driven models are proposed as an alternative to hydrological methods in sediment estimation calculations. The aim of this study was to compare the performance and accuracy of hydrological and data-based methods in estimating the amount of suspended sediment. For this purpose, discharge and sediment data were collected in the period of 20 yr (2001-2011) and then the amount of suspended sediment of Bagh Kalayeh hydrometric station on Alamut River in Qazvin province was estimated. In this study hydrological methods including Smearing, FAO and Sediment Rating Curves versus data driven methods including Gene Expression Programming, Instance-Based Learning with parameter K and Linear Regression methods were used. The model performances were compared using two statistical methods of RRMSE and NS. The results showed that two techniques such as IBK model with evaluation criteria of (R = 0.94, RRMSE = 0.29 and NS = 0.24) and the GEP model with (R = 0.85, RRMSE = 0.59 and NS = 0.65) estimated suspended sediment in more accurate way than other studies methods. Thus, the superiority of data-driven methods in estimating the amount of suspended sediment in the study area was proved. Therefore, the use of data-based techniques as a competitor and alternative to hydrological methods to estimate the amount of suspended sediment in areas similar to the study area is recommended.

In this research, the kinetics of removal of acidic blue 206, an acidic dye, from aqueous solution by multi-wall carbon nanotube at optimized pH of 6.5 and 298K has been investigated. Six kinetics models contain pseudo-first-order, pseudo-second-order, intra-particle, Elovich, Bingham, and modified Freundlich model used. Kinetic data were fitted by these six models using linear and nonlinear regression. The results showed that the pseudo-second-order model was best fitted in both linear and nonlinear regression methods. The rate-determining step was the surface adsorption. The calculated qe at pseudo-first-order and pseudo-second-order models, (73mg/g and 75mg/g, respectively) are near the experimental value (99 mg/g). The order of compatibility of data with other models at nonlinear regression is pseudo-first order> Bingham> modified Freundlich> Elovich> intra-particle diffusion models. For leaner regression the order of compatibility of data are Bingham> elovich> modified freundlich> intra-particle diffusion> pseudo-first-order models. The calculated Arrhenius activation energy was -61.63kJ/mol. The negative activation energy, Ea, means that there is no barrier energy on surface adsorption and the adsorption is exothermic.

Missing data are common issue in climate data. Also precipitation is a very important part of the hydrological cycle and meteorological and hydrological studies of watersheds, initially depend on the quantity and quality of recorded rainfall data and its distribution in the area. Complete and reliable sets of climatic and hydrological data are required for planning and design of these projects. Therefore for treatment of precipitation missing data, various methods have been developed and applied. Normal ratio method, linear regression, multivariate regression and inverse distance weighting (IDW) have a wide applications in natural resources study in our country. Therefore, it is necessary to determine the ability of these methods, especially in relation to the precipitation parameter, which plays a crucial role in the study of natural resources. In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of Iran was investigated for varying proportion of missing data from 5 to 50% of total data. In fact, the main purpose of this study is to answer the question of which of the four mentioned methods are more effective for infilling precipitation missing data. The daily data of Iran’s synoptic meteorological stations were used for the present study. Using the Run homogeneity test, the data homogeneity was investigated. Also, using graphical exploring data, and especially boxplot diagrams, outlier data were identified and flagged as missing data. The average annual precipitation and temperature of 400 stations were determined, and then based on these data their de Martonne coefficients were computed. In the next step, stations with de Martonne coefficient less than 10 were selected as arid climate. Among them, 73 stations that had sufficient data from 1986 to 2017 were distinguished. To evaluate each of the data reconstruction methods, part of the actual data was deliberately discarded from the original data and then reconstructed. Due to high volume of calculations, this process was programmed in MATLAB software. The results showed that each method had different functionality according to the conditions. Daily data are not well estimated using the normal ratio method to estimate the missing data less than the actual one. The use of linear regression method showed that in daily time scale, unlike the normal ratio method, the model accuracy in data reconstruction is higher. For linear regression approach, the distance between the fitted line between the observed and estimated data is small at first, and as the precipitation increases, this distance increases, indicating that the model is less accurate in estimating the extreme values. Given that the fitting line is below the 1:1 line, the linear regression method estimates the actual values below normal. The same results can be found for IDW producer. The multivariate regression method is more accurate for daily time series when the proportion of missing data are not considerable, but is generally very sensitive to the proportion of missing data. The normal ratio method is not suitable for reconstructing daily missing values, however it is more stable than other methods when missing data increase. In monthly time series, the performance of the IDW method and then the normal ratio is better. In annual series, linear correlation, normal ratio, and IDW have better performances, respectively. The findings of this study show that in general, the accuracy of reconstructions on annual scales is more than monthly and on monthly scales is higher than daily. This is due to smoother time series in the monthly and annual time series than the daily ones. Also it should be noted that the scale of current studies is in Iran. If the data from the reserved rain-gauge stations of the Meteorological Organization and the Ministry of Energy are added to this data, the accuracy of the methods is expected to increase. As the results of the present study show, the accuracy of the models decreases with increasing incomplete data ratio. Therefore, if new data is included in missing data processing, there is an expectation of better performance of each of these methods. Finally it should be considered that each method should be used in accordance with the given conditions, and therefore it is recommended to develop a software package for infilling missing data in Iran.

Air pollution is a global threat to public and environmental health, especially in urban areas. Therefore, modeling is used to control and planing concentration of pollutants. In this paper, a model is proposed based on linear regression for short term forecasting of CO, PM10 and O3 based on meteorological parameters, and the results are presented. Data of meteorological parameters including humidity, pressure, minimum and maximum temperature, wind speed and wind direction (Birjand Meteorological Organization), and air pollution data (CO, PM10, and O3 concentrations) from the Birjand weather organization were prepared and used as daily average. SPSS16 software was used for linear regression modeling. The results showed that the highest correlation coefficient for CO pollutant with minimum temperature was 0.53 and the lowest correlation coefficient with the value of 0.166 was wtih the wind direction. The maximum correlation coefficient of PM10 contamination with wind speed was 0.33 and the lowest correlation coefficient of this pollutant with a pressure was 0.882. Finally, the highest correlation coefficient of O3 contamination with maximum temperature was 0.50 and also with regard to the regression coefficient obtained for carbon monoxide (R = 0.33) compared to the other two pollutants, has been obtained better.

The objective of this study is the surveying spatial analysis of PM2.5 pollutants condition and the effects of atmospheric factors on it in the metropolis of Tehran. For this, the daily PM2.5 data measured by the controlling air quality and atmospheric parameters measured by Meteorological Organization in Tehran was used. The meteorological parameters used in this study include: temperature (minimum, average and maximum), relative humidity (minimum, average and maximum), wind speed (average and maximum), dew point and atmospheric pressure respectively. To study the spatial concentration of PM2.5, inverse distance weighting interpolation method was used. Inverse distance weighting model results show that in regions 7, 10, 20 and 21 have the highest concentration. Daily average PM2.5 concentrations during the study period showed that the highest concentrations at 18 June and the lowest was in the third April. It also shows the monthly average in January was the highest concentrations, while the lowest concentration is assigned to April.  Seasonal Concentration shows the highest concentration of PM2.5 in winter season. Then, the statistical correlation between PM2.5 and atmospheric parameters examined. For this purpose, Pearson correlation and multiple linear regression methods were used. Pearson correlation coefficient analysis shows that, PM2.5 has a direct correlation with air pressure and dew point, While a negative correlation with rainfall and wind speed. The relationship between PM2.5 as the dependent variable and atmospheric parameters as independent variables were investigated by linear regression models (Enter and Stepwise). The correlation coefficient in the equation Enter and Stepwise is 0.427 and 0.346 respectively. Which represents the ability of both models to predict the amount of PM2.5. Test Results of Mean Square Error, shows, Stepwise model to predict PM2.5, is more suitable than other methods.

Air pollution is a global threat to public and environmental health, especially in urban areas. Therefore, modeling is used to control and planing concentration of pollutants. In this paper, a model is proposed based on linear regression for short term forecasting of CO, PM10 and O3 based on meteorological parameters, and the results are presented. Data of meteorological parameters including humidity, pressure, minimum and maximum temperature, wind speed and wind direction (Birjand Meteorological Organization), and air pollution data (CO, PM10, and O3 concentrations) from the Birjand weather organization were prepared and used as daily average. SPSS16 software was used for linear regression modeling. The results showed that the highest correlation coefficient for CO pollutant with minimum temperature was 0.53 and the lowest correlation coefficient with the value of 0.166 was wtih the wind direction. The maximum correlation coefficient of PM10 contamination with wind speed was 0.33 and the lowest correlation coefficient of this pollutant with a pressure was 0.882. Finally, the highest correlation coefficient of O3 contamination with maximum temperature was 0.50 and also with regard to the regression coefficient obtained for carbon monoxide (R = 0.33) compared to the other two pollutants, has been obtained better.

Water pollution in recent decades is as serious threat to the human and natural ecosystem so that assessing changes water quality are one of the most important topics for desirable use . In this investigation has been studied the Azna river quality in Chamzaman Hydrometric station that used for dry period (April to September) and wet period (October to March).To evaluate the water quality were used the Piper, Schoeller, Wilcox and time-series diagrams. Results water river Azna in Piper diagram indicative water type is calcic bicarbonates. In studied river, according to the graph Schuler is evaluated good and desirable for drinking. According to statistical studies, the methods of data analysis led to be determined 11 principal components for the data set, the first three components with more than 70% variability, statistical population interpret. Also according to the graph plotted cluster the samples is plotted in four clusters contains: the first cluster SO4-2 and pH, second cluster K,third cluster Ca2 and in the fourth cluster TDS, EC, Cl-, Mg2, HCO3-, Na and TH. The highest correlation between the total dissolved solid and chloride is observed. According to meaningful level of 0/05 in Kolmogorov – Smirnov, all ions of Azna River have normal distribution. Linear regression analysis of cations, anions and electrical conductivity as the dependent variableversus of total dissolved solids as the independent variable show high correlation between TDS with EC and Cl.

Investigating the trend and fluctuations of the sea surface temperature (SST) data arecritically important for understanding the interactions between the oceans, the atmosphere and the land in various spatial and temporal timescales. Such analysis of the SST timeseries is also essential for the detection and modeling of climate change. The increase in the global SST is one of the primary physical impacts of climate change. Some recent investigations have shown that the fluctuations in SST data over the Persian Gulf and the western parts of the Indian Ocean regulates Iran's precipitation particularly over the southern districts. The present study was, therefore, motivated to analyze the trends in the SST data over the northwestern parts of the Indian Ocean waters containing 30 nodes of 2by 2 (longitude and latitude) grids for the period 1950-2009. These grids were spread over various parts of the Persian Gulf and Arabian Sea, the water bodied between 18 to 30 North and between 45 to 72 East. The monthly SST data was gratefully extracted from the database of the physical sciences division of the National Oceanic and Atmospheric Administration (NOAA). According to their geographical positions, these 30 grids were classified into three groups, namely, the Persian Gulf, coastal areas (i.e., the grids off the coasts by 2) and the Arabian Sea regions. Since the SST time-series generally have a normal distribution, a linear regression analysis was applied to detect the trend in the constructed time series for the classified regions in annual and seasonal timescales. The analysis was conducted by each grid individually as well as by averaging the SST data over each of the three mentioned zones. The seasonal time series were constructed by averaging monthly data so that winter, spring, summer and autumn consisted of the months Jan-March, April-June, July-Sep and Oct-Dec, respectively. The 60 years of the study period were also divided into three consecutive 20 years to assess the consistency in trend-line slope over time. The parametric statistical tests were used to investigate whether the detected trends are significant The study revealed that during the 60 years of the study period, the SST of these 30 grids has inclusively increased by about 0.61°C. It is in general agreement with Deser et al. (2010) that reported the magnitude of the global SST trend during the 1900-2008 period as approximately 0.4–1.0°C per century in the tropics and subtropics and 1.2– 1.6°C per century at higher latitudes. The amount of such increase for the Persian Gulf, coastal areas and the Arabian Sea was 0.5°C, 0.65°C and 0.63°C, respectively. Comparing with other seasons, for the large regions of the three classified zones the increasing trend was the greatest and the least for autumn and winter, respectively. While the spring and summer’s SST were increased by about 0.5°C during the last six decades, the corresponding increases for winter and autumn were found to be 0.60°C and 0.85°C, respectively. The autumnal upward trend was significantly greater than other seasons for the Persian Gulf and the coastal regions. However, the upward trend is statistically identical during autumn and winter over the Arabian Sea areas. With the exception of the spring, the slopes of the trend-lines were different between the Persian Gulf, coastal areas and the Arabian Sea during the other seasons.When the considered 60 year period was divided into three consecutive 20 yearperiods, the trend exhibited a variety of differences between these new shorter data sets. While the spring and wintertime SSTs did not exhibit any significant trend during either 1950-1969 or 1970-1989 periods, the upward trend was significant for the period 1990- 2009. In contrast to winter and spring, most of the considered SST time series (excluding the Persian Gulf data) were significantly warmed up during 1950-1969. No significant trend was observed for the period 1970-1989 on a seasonal scale. In spite of the fact that the Persian Gulf SSTs did not exhibit any significant positive trend during the summers or autumns for either 1950-1969 or 1970-1989 periods, the trend abruptly increased during the 1990-2009 period for these two seasons.