This study compares three interpolation methods to create continuous surfaces that describe temperature trends in Bangladesh between years 1948 and 2007. The reviewed techniques include Spline, Inverse Distance Weighting (IDW) and Kriging. A statistical assessment based on univariate statistics of the resulting continuous surfaces indicates that there is little difference in the predictive powe...

This study presents a method to estimate land surface temperature (LST) by calibrating spatial interpolation using satellite-derived surface emissivity. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Kriging, and Cokriging were tested to interpolate LST in southern New England using ground temperatures measured at national weather stations (NWS) in summer...

This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions an...

in this research, the spatial distribution of electrical conductivity and ph of springs, ghanats and the base flow concentration places on the streams of central and south-west and central parts of the hamedan-bahar plain were evaluated. using different geostatistical methods such as kriging, minimum curvature, inverse distance, natural neighbor, local polynomial and radial basis functions, 108...

The spatial distribution of precipitation is one the most important climatic variables used in geographic and environmental studies. However, when there a lack full coverage meteorological stations, estimations are necessary to interpolate for larger areas. purpose this research was find best interpolation method mapping partly densely populated Khorasan Razavi province northeastern Iran. To ac...

As the development of earth sciences and cross-disciplinary, it’s more and more meaningful and valuable to analyze and estimate precipitation spatial distribution. Precipitation spatial information is important in many fields, such as water resource management, drought and flood disaster predication, and regional sustainable development. It’s unrealistic to get the accurate predication of a par...