linear regression

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.

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.

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.