rainfall spatial analysis methods are very helpful since there are not enough rainfall gauge stations and watersheds are scattered in large extent. there are many different methods for estimating average precipitation such as; arithmetic method and thiessen polygon. however, the arrangement and location of data and their correlations are not considered by classic methods. thus, geostatistical t...

Many spatial interpolation methods perform well for gentle terrains when producing spatially continuous surfaces based on ground point data. However, few interpolation methods perform satisfactorily for complex terrains. Our objective in the present study was to analyze the suitability of several popular interpolation methods for complex terrains and propose an optimal method. A data set of 153...

Tea components (tea polyphenols, catechins, free amino acids, and caffeine) are the key factors affecting quality of green tea. This study aimed to relate biochemical substances in tea soil nutrient composition effectiveness fertilization. Seventy samples their corresponding plantation were randomly collected from Xinyang City, China. The caffeine examined, as well pH, nitrate (NO3--N), ammoniu...

estimation and mapping of forest resources are preconditions for management, planning and research. in this study, we applied kriging interpolation of geostatistics for estimation and mapping of forest stock at-tributes in a natural, uneven-aged, unmanaged forest in the caspian region of northern iran. the site of the study has an area of 516 ha and an elevation that ranges from 1100 to 1450 m ...

ABSTRACT Decision makers will in an emergency situation often be interested in predictions of harmful environmental variables relative to a certain threshold. We are therefore examining the robustness of different methods which are supposed to give unbiased estimates relative to such a threshold, both for point predictions and areal averages. We are also interested in exceedance probabilities. ...

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFO...

Accurate rainfall data are essential for environmental applications in the actual assessment of geographical distribution rainfall. Interpolation methods usually applied to monitor spatial data. There many interpolation methods, but none them can achieve all cases best results. In this study, three different were investigated with regard their suitability producing a distribution. Rainfall from...

Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage ML is their flexibility use various layers information covariates. However, come many variations that can make application by end users difficult. To fill this gap, a Smart-Map plugin, which complements Geographic Information System QGI...