The climatic circumstances in a local play a significant role in human, animals, and plants distribution. Thus, without knowing the climate, any activity or planning in different fields economic, agriculture and industrial, is no possible. So Climatic Regionalization or knowing different climatic regions is necessary for planning and territory sustainable development. the aim of the present recognize and study the most influential climatic elements affecting on the climate of zayandehrood basin, and the spatial separation different climatic regions by using Multivariate statistical methods. To do so, the mean annual data of 27 climatic elements from 12 synoptic stations in a 20 years period were used in the limit of zayandehrood basin. Then the pointed data was changed in to a matrix 27×1658 by contribution of the capabilities of surfer’s software statistical land and with the help of Kriging interpolation with pixels 5×5 kilometer. In processing data has been used the new method on the base of statistical techniques principal components analysis and cluster analysis by ward method. Analysis works of the principal components on the data correlated matrix showed that with 6 principal components, we can describe 98.6% of variance in data. Tree component (thermal, windy, dusty-windy, dusty) expresses the most variance (92%) data. the scores of the principal components was analyzed by "agglomerative hierarchical cluster analysis", in "ward" method that the zayandehrood basin was divided to 5 regions.

The climatic circumstances in a local play a significant role in human, animals, and plants distribution. Thus, without knowing the climate, any activity or planning in different fields economic, agriculture and industrial, is no possible. So Climatic Regionalization or knowing different climatic regions is necessary for planning and territory sustainable development. the aim of the present recognize and study the most influential climatic elements affecting on the climate of Zayandehrood basin, and the spatial separation different climatic regions by using Multivariate statistical methods. To do so, the mean annual data of 27 climatic elements from 12 synoptic stations in a 20 years period were used in the limit of Zayandehrood basin. Then the pointed data was changed in to a matrix 27×1658 by contribution of the capabilities of surfer’s software statistical land and with the help of Kriging interpolation with pixels 5×5 kilometer. In processing data has been used the new method on the base of statistical techniques principal components analysis and cluster analysis by ward method. Analysis works of the principal components on the data correlated matrix showed that with 6 principal components, we can describe 98.6% of variance in data. Tree component (thermal, windy, dusty-windy, dusty) expresses the most variance (92%) data. the scores of the principal components was analyzed by “agglomerative hierarchical cluster analysis”, in “ward” method that the Zayandehrood basin was divided to 5 regions.

Evaluating the performance and ranking of the judiciary of the provinces periodically is always of interest. This problem can be done with univariate approaches,But considering many variables involved in this matter, the use of Multivariate methods is more justified. A new method for this is ranking based on the depth concept. In this paper, the performance of prosecutors’,offices, criminal courts and appeals courts of the provinces have been ranked over one year based on two indicators,case processing rate and the case congestion rate, employing the depth Multivariate method. Evaluating the results of ranking intuitively confirms the appropriateness and rationality of this approach.

Land surface emissivity (LSE) is an important intrinsic property of materials and knowledge of the LSE is essential to derive the land surface temperature (LST) that can be obtained from the emitted radiance measured from space. LSE provides useful information for geological and environmental studies, mineral mapping and is one of the important input parameters for climate, hydrological, ecological and biological models. The emissivity of natural surfaces inherently may vary significantly due to differences in soil structure, soil composition, organic matter, moisture content and differences in vegetation cover characteristics. In other words, LSE changes is depending on the surface (such as texture, topography, soil moisture, angular variations effect) and sensor parameters (such as spatial resolution, SRF, and effective wavelength of thermal bands). Remote sensing technology provides widely the monitoring of this quantity. Several methods exist to estimate LSE from satellite data, which apply the visible and near-infrared (VNIR) or thermal infrared (TIR) spectral regions or both of them. According to the way by which the LSE is determined along with LST, the emissivity estimation methods from optical remote sensing data can be categorized into three distinct types including, stepwise retrieval methods, simultaneous LST and LSE retrieval methods with known atmospheric parameters, and simultaneous LSEs, LST, and atmospheric quantities retrieval methods. Influential researches, in the stepwise retrieval methods, were conducted and mainly NDVI-methods have been used to predict LSE from NDVI values. In particular, NDVI-methods assume that the surface is composed of the soil and vegetation, some problems arise for other kinds of surfaces that are likely classified as bare soil pixels, such as rocks, man-made, and ice/snow. Besides, another main origin of error in these methods is caused by great changes in the emissivity of soil types. Furthermore, the choice of a typical emissivity value for some surface objects such as bare soil is a more critical question, because the variability of emissivity values for soils is more than vegetation and other ones. In this research, a new approach called improved normalized difference vegetation index-based method (INDVI_based) estimating LSE on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in bare soil, vegetation and mixed areas is accomplished based on convolving ASTER spectral Library with LDCM spectral response functions. Then, for main three areas are defined to determine separate emissivity estimate model as function of reflective bands from basic spectra associated with the main class. In the proposed method, the cannel LSEs are expressed as functions of atmospherically corrected reflectance from the LDCM visible and near-infrared channels with wavelength ranging from 0.4 to 2.29 μm fo bare soil. The effectiveness of the proposed approach was implemented in LDCM data and obtained LSE were compared and validated with two scenes of LSE standard product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results showed that LSE of the improved proposed method, in the band 10 of LDCM in comparison with the first and second LSE product of ASTER, lead to 0.76% and 0.75% errors in term of root mean square error (RMSE) measure, respectively. Moreover, this error for thermal band 11 is1.49 % and 1.06% in first and second examined scenes, respectively. Unlike previous methods, the proposed method not only accurately estimates of LSE as a function from the reflectance of various surface objects, but also it uses the spectral response function of thermal and reflective bands in estimating the LSE. In addition, the proposed method the poor relationship between LSE and only reflectance of the red band in previous methods, strengthen due to the use of all reflective bands in LSE estimation and it is applicable on most sensors.

In basins of the northern part of Iran, combination of natural and human factors has intensified landslide and related numerous damages. The investigation importance of this phenomena and effective factors of landslide occurrence and also determining susceptible zones it order to preventing and avoiding its effects is required. At the beginning, of this research after field investigation, review of previous works in similar Lajemrood basin and using questionnaire, were recognized primary effective factors on landslide occurrence. Thin preparing effective factors maps in Geographic Information System. In order to quantifying the factors and weighting them, landslides percentage in different units was determined. For statistical analysis of multiple regressions the stepwise method was used which concluded with ignoring the elevation factor as regional model. Finally, geology, land use, slopes and distance from the road has the most effects on landslides. For evaluation of the accuracy of this model, neighboring basing (Varakee basin) with similar characteristics was chosen. The results have indicated that the most recorded area landslides in Varaki basin occurred in high and high hazard zonation. Thus, it can be concluded that presented model can be used for the study area.

methods for evaluatingthe quality of groundwater resources and recognition of appropriate locations for urban and agricultural water demand has been known as an important item in water resources planning. The main focus of this paper has been addressed a new water quality mapping based on coupling of fuzzy approximation and water quality indexing. In this paper, after indexing water quality in each monitoring well, based on fuzzy reasoning a new global fuzzy indexing has been presented. In this regard, 12 water quality parameters for 217 monitoring wells in the province of Ghazvin in the north have been used. In the final step, map of fuzzy evaluation over the area of interest has been provided based on an optimized new spatial approximation via Genetic Algorithm (GA). The results showed the capability of the proposed methodology for groundwater quality mapping. The most important contribution of this paper is successful combination of fuzzy reasoning and water quality indexing as a continuous function for evaluating groundwater quality mapping.

Attempts to obtain experimental values for the kinetic parameters of phenanthridine oxidation by guinea pig or rabbit liver aldehyde oxidase using common spectrophotometric methods have not been successful due to a lower limit of detection. In the present study, a new spectrofluorimetric assay in combination with a Multivariate calibration method for enzymatic kinetic study of aldehyde oxidase activity, using phenanthridine as the substrate, has been developed and validated. Phenanthridine and phenanthridinone binary mixtures were prepared in a dynamic linear range of 0.025-1 mM and the emission flourimetric spectra of the solutions recorded at the excitation and emission wavelengths of 236 and 320-450 nm, respectively. The optimized calibration model of partial least squares (PLS) method was applied for the simultaneous determination of the concentration of each chemical in the prediction set. The limits of detection for phenanthridine and phenanthridinone were found to be 2.13 ± 0.33 and 3.41 ± 0.34 nM (mean ±  SD, n = 5), respectively. This method was then used for kinetic study of phenanthridine oxidation using guinea pig and rat hepatic aldehyde oxidase. The results were compared with those obtained from a univariate spectroscopic method. Using this new spectrofluorimetric-Multivariate calibration method, the Km value for the oxidation of phenanthridine with guinea pig and rat liver aldehyde oxidase were obtained as 0.83 ±  0.08 and 2.20 ±  0.40, mM (mean ±  SD, n = 3), respectively.

Mighan playa is the most important extraction point of sodium sulfate in Iran. The playa consists of two parts, the island in the center and the lake in the margin. Extraction of sodium sulfate on the island is associated with the formation of artificial ponds. Sodium sulfate company effluent and treated wastewater are the most important sources of surface water in Mighan playa. The effects of salinity over groundwater quality is one of the objectives in this article. The univariate and Multivariate statistics such as mean comparison test, factor, cluster and discriminant analysis was used to compare saline playa with groundwater resources. In 22 samples, it was determined variables of electrical conductivity, alkalinity, total soluble solids, pH, calcium, magnesium, potassium, sodium, chlorine, sulfate and bicarbonate. It identified three water groups such as pond brine, lake brine and groundwater by differences in total soluble solids, pH, sulfate and chlorine. Multivariate analysis showed that qualitative variables are of several origins. The salinity of Mighan playa water and groundwater is related to anions and cations that originate from the gypsum and saline layers of the northern part, alkalinity and bicarbonate due to the dissolution of limestone in the southern floors of the Mighan catchment. Recent compounds are the result of groundwater and the entry of treated Arak municipal wastewater into the playa. Evaluation of the sodium to chlorine ratio and Gibbs diagram show the influence of infiltration Mighan playa saline water on the groundwater, especially in the playa margin.