Management decisions whose environmental impacts affect directly or indirectly surface waters must of necessity be based on adequate knowledge and information when water quality zoning and a clear picture of river water quality are sought. Water quality zoning is based on pollution criteria that are identified on the basis of different water quality parameters drawn from historical data and the water uses in the region. The aggregate of the data and parameters involved make river water quality modeling a complex process. In this paper, the Principal Component Analysis ((PCA)) is used to reduce the water quality parameters involved in the identification of river water pollution criteria. The method keeps those Components with more variances. The results show that the first Component transfers 93.59% of the variation in the data, while the first two and the first six Components explain 96.67% and 99.99% of the variations, respectively. Based on the criteria thus identified, the fuzzy clustering Analysis is used in a second stage of the study to classify the river intervals. For this purpose, the fuzzy water quality data are provided to generate the fuzzy similarity matrix based on the fuzzy relations. Then, the stabilized matrix and the clustering diagram are created. Finally, the river intervals are classified into similar categories using the proper thresholds. The efficiency of the proposed method is evaluated by employing water quality data collected from the Zayandehrood River monitoring stations.

Elevated concentrations of particulate matter (PMs), particularly PM2. 5, are significantly influenced by various anthropogenic activities, including industrial processes, population growth, and fossil fuel combustion, especially during peak urban hours. The burgeoning volume of environmental data often leads to crucial decisions being made with inadequate information. Data mining techniques offer a powerful approach to extracting knowledge, compress data, and facilitate informed environmental decision-making. Regarding PM2. 5 in Isfahan, understanding the characteristics and origins of each monitoring station is paramount. Specifically, determining the influence of various factors on each station and classifying them based on pollution sources is crucial. Principal Component Analysis ((PCA)) was employed for this purpose. This study suggests that the urban stations (Parvin, Kharazi, Rodki, Ahmadabad, and Ostandari) are likely heavily influenced by fossil fuel combustion from transportation and building heating. The Estandari station, located in the city center with high traffic density, requires special attention due to its relatively large green space, which may influence particulate deposition and accumulation. The Segzi plain, characterized by severe wind erosion, predominantly exhibits PMs of natural origin. Mitigation strategies, such as mulching or afforestation with drought-resistant plants, are necessary to reduce wind erosion and subsequent particulate dispersion. Finally, the Mubarakeh area, a significant industrial hub in Isfahan, displays PMs primarily originating from industrial activities.

Corporate responsibility for sustainability issues has attracted much global attention in recent years due to disasters such as pollution and ozone layer depletion. In the meantime, the demand for companies to be accountable for their sustainability performance has begun, with a concomitant growth in the assurance of sustainability reporting. Therefore, this study aims to the requirements, challenges, and consequences of sustainability reporting assurance using the Principal Component Analysis method in Iran from the perspective of experts and professionals. This study was conducted in 1403 through a questionnaire with 151 experts, including university faculty members, audit organization employees, and practitioners in the audit profession. The resulting data were analyzed using the Principal Component Analysis method. The findings showed that among the Components related to sustainability reporting assurance requirements, the Components of "encouragement", "culture", and "law",Also, among the Components related to challenges, the Components of "Infrastructure and Technology", "Cost" and "Culture" and finally, among the Components related to consequences, the Components of "Company Performance and Value", "Negative Consequences" and "Environment" are ranked first to third, respectively. This research helps to improve the awareness of all stakeholders about the trends in the field of information verification in the field of sustainable development.

In order to efficiently manage groundwater resources, determination of the main sampling points is very important to reduce sample size and save time and cost. Principal Component Analysis ((PCA)) is one of the data reduction techniques that has an important role in identifying insignificant data. In this research, 22 wells of Gonabad plain with a statistical length of 10 years (2007-2016) were used. In the studied area, the annual average of 11 quality parameters of Ca, Mg, Na, EC, TDS, Cl, SAR, HCO3, SO4, TH, pH groundwater was investigated by using this technique to determine the quality effective wells in the aquifer of this plain. Using (PCA), the relative importance of each well was calculated between 0 (for completely ineffective well) to 1 (for the very effective wells). The results showed that among the 22 wells in the study area, 7 wells were identified as the quality effective wells of Gonabad plain, which had a good dispersion in the region and could play an important role in reducing sampling costs.

Estimation of plant water requirement is one of the most important needs of the agricultural activity which can play an important role in the proper use of water resources. The first step for calculating of plant water requirement is the estimation of reference Evapotranspiration. According to this fact that the estimation of potential evapotranspiration needs lots of meteorological parameters, the aim of this research is to obtain a simple equation for estimating of evapotranspiration, using Principal Component Analysis in Ardabil and Tabriz. For this aim parameters including temperature (maximum, mean and minimum), relative humidity, sunshine hours, precipitation and the wind speed in daily scale for a period of 1962-2016 for Tabriz and 1992-2015 for Ardebil is used. The results of Principal Component Analysis reduce these parameters to two and three Components (PC) for Tabriz and Ardebil respectively. These PC explain the 78% of parameter’ s variance in Tabriz and 83% in Ardebil, respectively. By using of these Components, new equations are obtained for calculate the potential evapotranspiration. The results of evapotranspiration modeling show that the coefficient of determination between daily reference evapotranspiration and Principal Components (PC) for calibration and verification periods are 0. 53 and 0. 69 for Tabriz and 071 and 0. 73 for Ardebil, respectively. Also, the Nash coefficients for Tabriz are 0. 61 and 0. 61 and for Ardebil are 071 and 0. 73 which showing the appropriate performance of models. The results also show that the evapotranspiration in Tabriz is highly affected by temperature parameter, relative humidity and sunshine hours and in Ardebil is only affected by temperature.

Introduction1 Development of high-power and cost-effective genotyping methods in recent years has provided the possibility of evaluation the genetic structure and the relationship among species populations utilizing genomic data. Genome wide inference of population structure using genetic markers could provide invaluable information associated with evolutionary relationships and clustering of subpopulations for performing animal breeding programs. In large scale studies, one of the interesting subjects is to study the existence of genetic differences among subdivided groups ascertained from different geographic locations. The objective of this study was to compare the Principal Component Analysis ((PCA)) and discriminant Analysis of Principal Component (DAPC) approaches for determining the population structure and study how an individual allocated to the true population of origin, in three Horse breeds located in Middle East consisting Akhal Take, Arabian and Caspian using genomic data. Materials and Methods In this study, the genomic data obtained from 61 animals consisting Akhal Take (19), Arabian (24) and Caspian (18) were used to investigate the population structure of some Asian horse breeds. The data were obtained from the Equine Genetic Diversity Consortium (EGDC) project. Hair or tissue samples were collected from animals. DNA extraction was performed using an optimized Pure gene (Qiagen) assay and approximately 1 μ g of DNA was used for genotyping of the samples. Genotyping was performed using Illumina SNP 50K BeadChip arrays that allow to genotype 52603 SNP marker loci, according to the Illumina standard guidelines. In this study, different quality control steps were applied on preliminary data to ensure the quality of genotyping data. Quality control carried out using PLINK v. 1. 07 program. The samples with more than 5% missing data were excluded from Analysis. Then for each SNP, MAF and call percentage were calculated and the SNPs with a call rate<95% and a MAF<2% were discarded. Deviation from Hardy-Weinberg equilibrium (p<10-6 ) was estimated for the remaining SNPs to identify genotyping errors. The Bonferroni correction (β =α /n) was used to address the multiple testing comparison problem. Principal Component Analysis ((PCA)) is a statistical technique for summarizing data from many variables into a few variables which describe as much of the variation in the data as possible. For this purpose, the variance-covariance matrix of independent variables was first calculated and Principal Components were extracted. Each new variable has an associated Eigen value that measures the respective amount of explained variance. Furthermore, the model independent of discriminant Analysis of Principal Component (DAPC) is a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Analysis was performed using (PCA) and DAPC approaches and the codes for Analysis were provided in R v. 3. 4. 1 software. Results and Discussion The Analysis of the main Components summarizes the general variation among individuals, which includes both the variability between the groups and the diversity of the groups, and shows a clear picture of the differences between the groups. The results of this study indicated that 10. 8% of the variance was explained by the first two Components in both (PCA) and DAPC methods. Both methods showed high accuracy for assigning of individuals to the true population of origin and both were able to cluster three populations separately. The Bayesian information criterion (BIC) index was used for evaluating the optimal number of clusters for DAPC method and the results revealed that K=3 showing the optimal number with lowest BIC that completely separate three populations. The DAPC method was better than (PCA) to separate populations from each other due to the increase of intergroup variance and the reduction of intra-group variance. In determining the optimal number of K, it worked better than (PCA) method and provided a better picture of the relationship between individuals. This results show that DAPC method can be applied in quality control of GWAS as an alternative to the (PCA), because of summarizing the genetic differentiation between groups and overlooking within-group variation and provides better population structure. Conclusion In general, the results of this study showed that although the previous studies grouped these three breeds located in Middle East in one cluster of neighboring trees, however, according to the results of this study, three breeds are grouped separately, and the DAPC method can better illustrate the inter-population relationships in horse breeds.

Evapotranspiration is one of the most important parameters that its understanding is necessary for estimating crop water requirement and design of irrigation systems. This phenomenon is greatly influenced by climatic parameters. In this study, the relative importance of variables affecting this phenomenon was evaluated and the reference evapotranspiration was estimated using Principal Component Analysis and factor Analysis. Daily scaled measurements for the period of 1991-2005 were obtained from synoptic stations located in Mashhad Khorasan Razavi provience, Iran. Mashhad has a semi-arid climate area. The measurements included the relative influence of temperature (T) (maximum, average and minimum), relative humidity (RH), sunshine hours (Rs), and the wind speed at a height of two meters above the ground (U2). The multiple linear regressions were used to estimate evapotranspiration. T-statistic with a significant level of 5% was used for the main Components. The evapotranspiration was correlated more with T (minimum. maximum, and average), and relative humidity as than wind speed or sunshine. PC1 had more effect than PC2 (with coefficients of 0.694 and 0.556, respectively). MLR-(PCA) and MLR with coefficients of 0.903 and 0.897 (respectively) indicated higher ability for (PCA) method.

The ionosphere is a layer of Earth's atmosphere extending from an altitude of 100 to more than 1000 km. Typically total electron content (TEC) is used to study the behavior and properties of the ionosphere. In fact, TEC is the total number of free electrons in the path between the satellite and the receiver. TEC varies greatly with time and space. TEC temporal frequencies can be considered on a daily, monthly, seasonal and annual basis. Understanding these variations is crucial in space science, satellite systems and positioning. Therefore, ionosphere time series modeling is very important. It requires a lot of observations to model the ionosphere temporal frequencies. As a result, it requires a model with high speed and accuracy. In this paper, a new method is presented for modeling the ionosphere time series. The Principal Component Analysis ((PCA)) method is combined with the fuzzy inference system (FIS) and then, the ionosphere time series are modeled. The advantage of this combination is to increase the computational speed, reduce the convergence time to the optimal solution as well as increase the accuracy of the results. With the proposed model, the ionosphere can be analyzed at shorter time resolutions. Principal Component Analysisis a statistical procedure that uses anorthogonal transformationto convert a set of observations of possibly correlated variables into a set of values oflinearly uncorrelatedvariables calledPrincipal Components. This transformation is defined in such a way that the first Principal Component has the largest possiblevariance, and each succeeding Component in turn has the highest variance possible under the constraint that it isorthogonalto the preceding Components. The resulting vectors are an uncorrelated orthogonal basis set. (PCA) is sensitive to the relative scaling of the original variables. Fuzzy inference systems (FIS) take inputs and process them based on the pre-specified rules to produce the outputs. Both the inputs and outputs are real-valued, whereas the internal processing is based on fuzzy rules and fuzzy arithmetic. FIS is the key unit of a fuzzy logic system having decision making as its primary work. It uses the “IF…THEN” rules along with connectors “OR” or “AND” for drawing essential decision rules. To evaluate the proposed method of this paper, observations of Tehran's GNSS station, in 2016 have been used. This station is one of the International GNSS Service (IGS) in Iran. Therefore, its observations are easily accessible and evaluated. The statistical indices dVTEC = |VTECGPS-VTECmodel|, correlation coefficient and root mean square error (RMSE) are used to evaluate the new method. The statistical evaluations made on the dVTEC show that for the (PCA)-FIS combination model, this index has a lower numerical value than the FIS model without (PCA) as well as the global ionosphere map (GIM-TEC) and NeQuick empirical ionosphere model. The correlation coefficients are obtained 0. 890, 0. 704 and 0. 697 for (PCA)-FIS, GIM and NeQuick models with respect to the GPS-TEC as a reference observation. Using the combination of (PCA) and FIS, the convergence speed to an optimal solution decreased from 205 to 159 seconds. Also, the RMSE of training and testing steps have also been significantly reduced. Northern, eastern, and height Component Analysis in precise point positioning (PPP) also show higher accuracy of the proposed model than the GIM and NeQuick model. The results of this paper show that the (PCA)-FIS method is a new method with precision, accuracy and high speed for time series modeling of TEC variations.

The purpose of this paper is to explain the relationship between financial development and poverty in Iran. Considering the existence of different indicators in the financial development literature, in order to introduce a combination of the variables for financial development, the Principal Component Analysis ((PCA)) method was used. This method takes into account most of the dimensions of financial development to construct a composite index. The Poverty Index (SST) was also used as a poverty indicator. In order to test the relationship between the variables, the Autoregressive Distributed Lag model (ARDL) was used. After the second-rate financial development index was introduced, the non-linear effects of the relationship between financial development and poverty were evaluated for the period of 1395-1368. The results indicated that the financial development variable had a negative and significant effect on poverty. In other words, improving the financial situation would lead to poverty reduction in the society. The coefficient of the second power index was negative and significant, indicating that there is a reverse U relation in the case of the financial development and poverty in Iran.