جستجوی مقالات مرتبط با کلیدواژه « درونیابی » در نشریات گروه « جغرافیا »

Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different mathematical and statistical models to predict the unknown values. The similarity of the unknown points to the nearest known points or the principle of the nearest neighbor is the basis of interpolation methods, and how this principle is used depends on the selected model. In a general classification, interpolation methods are divided into two large classes. The first method is deterministic, in which interpolation is carried out based on determining the level of sampled points and also based on the similarities such as Inverse Distance Weighting (IDW) method or Radial Basis Function (RBFs). In the second method, interpolation is probabilistic – geostatistical, that is done based on the statistical properties of the sampled points. On the other hand, due to the growing increase in the problems of urbanization and urban heat islands, current cities need to have a detailed planning for future developments and preserving the quality of urban environment. Also, the geometry of urban valleys, which is defined by changing the height, length and distance of buildings, has a significant impact on the energy exchange and thus, the temperature of urban areas. But, this temperature, in turn, depends on a number of geographical - geometric factors (such as SVF) and meteorological variables. The Sky View Factor (SVF), as one of the usual indicators of describing urban geometry that refers to the amount of sky observable from a point on the Earth, has become one of the most important predictors of UHI due to its applicability in the urban climate, its contribution to the spatial data, and the existence of available techniques. In the climatic studies, the SVF is also considered as an important geometric parameter due to its correlation with the local temperature performance and its potential importance in the urban design process.Although urban Climatologists know this indicator well, it is not that much known among the urban designers and planners. This issue has not progressed much in Iran and there are no reliable sources about it. Despite the fact that different methods and models have been introduced for interpolation of Point data, no specific method has been proposed for estimating this index. Hence, this study has empirically compared the interpolation models with an emphasis on the Empirical Bayesian Kriging (EBK). This comparison is important since EBK has automated the most difficult aspects of the construction of a kriging model. This is while in other Kriging methods, the parameters are adjusted manually to obtain accurate results. EBK automatically simulates and calculates these parameters through a setup process. In classical kriging, it is also assumed that the estimated semivariogram is a true semivariogram of the observed data. This means that the data are generated from Gaussian distribution with the correlation structure defined by the estimated semivariogram. This is a very strong assumption, and it rarely holds true in practice. Accordingly, measures should be taken to make the statistical model more realistic. 

 The present study is an applied research in terms of its objective and it is quantitative in terms of the data analysis method. The study area is district 6 of Shiraz Municipality (496 hectares). Due to the multiplicity of interpolation methods and techniques as well as kernel functions and model fit functions, about 138 interpolation scenarios arewereimplemented. Also, four indices of Root-Mean-Square (RMS), Mean Standardized (MS), Root-Mean-Square Standardized (RMSS) and Average Standard Error (ASE) have been used for evaluating the models. The input data (sample) contains 6157 points, measured at intervals of 30 m distances in the study area. These points are werecreated based on the SVF calculation software method and using the GIS base model in ArcGIS10.6.  

Out of 138 scenarios, seven scenarios with the lowest RMS values arewereseparately examined in detail taking into account three other indicators. Another variable called “Neighborhood type” iswas added to the surveys in two standard and smooth modes. The results show that simple kriging and EBK have better results than the other models. Also, among the simple Kriging fitted models, the RQ model shows better results than other fitting models.  

Based on the RMS index, EBK is one of the best reliable automatic interpolation models (ranked second) for estimating the SVF. In general, based on RMS, MS, RMSS, it is the best automatic interpolation model for estimating SVF.

As the biggest metropolis of Iran, Tehran is among the most polluted cities of the world. Due to the detrimental effects of air pollution on health and environment, determining the exact pollutant and areas of pollution is necessary. The aim of this study was to investigate the ability of MODIS data. This study measure particulate matter (particle pollution) in urban areas and make air quality maps using these data and the ground data. Data related to the pollutants pm10, o3, co, so2,pm2.5 in 1394 using simple kriging method as a method of  ground statistics in different modes were evaluated and maps of pollutants levels were drawn. In addition, satellite images of the first level and second level data of this sensor for qualitative and quantitative analysis of particulate matter was used in large areas. Then, using the Normalized Difference dust Index (NDDI) related to the first level data and parameters such as optical depth and Angstrom view related to the second level data, their correlation and RMSE with ground data related to the pollutant PM10was calculated. Results indicate that there was proper correlation between satellite images parameters and ground measurements. Discussion and This correlation indicates the images ability of this sensor to detect atmospheric dust. Also, in this study the role of meteorological parameters influencing air pollution was investigated and results indicate that the dominant pollutant index in the year94, was the particulate matter pm2—.5and PM10.Continuous stability of weather condition is among the effective factors that increase the density of pollutants that leads to the pollutants accumulation.

Rainfall is one of the most important natural sources of water used by mankind foragricultural and industrial applications. Because of the difficulty of rainfall monitoring,which has high spatial and temporal variability, our knowledge of both the its synopticand climatological distribution over most of the country is not well known.Climatological investigations in Iran lack adequate meteorological observations tounderstand and interpret diverse climatic features. In the country as a whole, theground observation network is not dense enough to provide the detailed informationrequired, especially in rugged regions. Combined with this is the problem of pooraccessibility to the mountainous regions, which has resulted in the areal distribution ofrainfall being poorly known. In addition, the data collected by the existingmeteorological stations, with a few exceptions, are mostly discontinuous, nonhomogenousand short period observations. Such limited data sets, make it difficult toboth accurately delimit different climatic regimes across the country and identifysignificant departures from normal conditions, whereas many climatologicalapplications and investigations should ideally be based on the data collected at givenpoints over long periods and should have a good spatial and temporal coverage.Perhaps the most urgent problem facing rainfall measurement at the present time isdata collection - one of the most costly areas in meteorology and hydrology.In this paper, the central aim is to investigate the potential of GeographicalInformation System for monitoring Fars province’s rainfall with special reference toKriging and IDW (Inverse Distance Weighting), to describe pre-processingapproaches, including relative calibration, and to examine various techniques for bothrainfall monitoring.Analyses of the GIS techniques provided very useful information on both thespatial and temporal distributions of rainfall over regional space, although datacollected by Kriging technique showed more accurate than IDW technique. In order toexplore the appropriate method, the accuracy of the obtained results from differentmethods using RMS was examined. The results showed that among different usedkriging methods, the circular and exponential ordinary had lowest error.