Evaluation of Different Interpolation Techniques in GIS for Rainfall Data in Sri Lanka (2013-2018) and Identification of the Validation of the Techniques

Abstract

Geographic Information Systems (GIS) and modelling are becoming powerful tools in geographical and agricultural research. Most data for environmental variables are collected from point sources. The spatial array of these data may enable a more precise estimation of the value of properties at unsampled sites than simple averaging between sampled points. Therefore, the ArcGIS software for interpolation technique was used to identify those differences in terms of rainfall variations. The main objective of this study is to identify the different interpolation techniques for rainfall data and evaluate the most accurate method for Sri Lanka. This study mainly collected rainfall data for weather stations in Sri Lanka. That data was collected in different categories. In this study, only 23 minor meteorological observatories were used for the accuracy assessment of the interpolation methods. For use, all stations are increasing time and cost, and data collection is very difficult. Therefore, 23 minor meteorological observatories were selected randomly from all over the study area. Five interpolation techniques were used. Researcher mainly used the Cross-Validation Method to identify the most suitable interpolation method for rainfall data in Sri Lanka. The main station’s data helped predict rainfall all over the study area, and deleted locations were used to extract estimated rainfall data. Finally, all estimated rainfall data was compared using their actual rainfall values to determine the accuracy of interpolation methods, and the statistical correlation method was used to determine the appropriate interpolation technique for the rainfall data. All estimated values are extracted from rainfall surfaces using an ArcGIS tool called “Extract Values from Points”. Those values are compared with actual observed rainfall values for these sub-stations for the same period. According to this method, then used the correlation statistical (F test) method to reveal the variation of both data sets. F tests were done using SPSS and F values. According to the correlation analysis, the suitable interpolation surfaces are the Spline and Kriging. Consideration of the other four methods (IDW, Kriging (Ordinary), Spline, and Natural Neighbour) shows some similarities. However, finally, researcher identified that the Spline method shows an R value of 0.99 while others show low values. Therefore, the Spline method was found to have the greatest efficiency and be the best method of all five.
 Keywords: ArcGIS, Inverse distance weighted, Interpolation, Kriging, Spline