so far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. in order to evaluate annually precipitation spatial variation, this study was conducted in gilan province which precipitation is...

The spatial pattern of precipitation is known to be highly dependent on meteorological conditions and relief. However, the relationships between precipitation and topography in mountainous areas are not very well known, partly because of the complex topography in these regions, and partly because of the sparsity of information available to study such relationships in high elevation areas. The p...

1. Abstract This paper presents a methodology that combines logistic regression with kriging for incorporating exhaustive secondary information into the mapping of the risk of occurrence of unexploded ordnance (UXO). Logistic regression, which is appropriate for binary data (indicators) analysis, is used to derive the trend component in simple kriging with varying local means (SKlm). The techni...

in this paper, two methods have been used: multi-layer perceptron artificial neural network (ann-mlp) and universal kriging to estimate of velocity field. neural network is an information processing system which is formed by a large number of simple processing elements, known as artificial nerves. it is formed by a number of nodes and weights connecting the nodes. the input data are multiplied ...

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with...

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study w...

Lidar data provide accurate measurements of forest canopy structure in the vertical plane however current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would therefore be desirable to integrate lidar and L...

Soil organic matter (SOM) is an important component of soils, and knowing the spatial distribution and variation of SOM is the premise for sustainably utilizing soils. The objective of this study was to compare geographically weighted regression (GWR) with regression kriging (RK) for estimating the spatial distribution of SOM using field-sample data in SOM and auxiliary data in correlated envir...

Accurate mapping of total soil C on the field scale is essential for evaluating efforts to sequester soil C and for providing individual producers with information on C sequestration potentials of their fields. Data on easily measured secondary variables that are strongly related to soil C are believed to be helpful in improving mapping accuracy. The objective of this study was to assess improv...