A Geostatistical Approach to Predict the Average Annual Rainfall of Bangladesh

Mohammad Samsul Alam ； Syed Shahadat Hossain

《Journal of Data Science》 14卷1期 (2016/01) Pp. 149-165

https://doi.org/10.6339/JDS.201601_14(1).0009

並列摘要

In this paper we tried to fit a predictive model for the average annual rainfall of Bangladesh through a geostatistical approach. From geostatistical point of view, we studied the spatial dependence pattern of average annual rainfall data (measured in mm) collected from 246 stations of Bangladesh. We have employed kriging or spatial interpolation for rainfall data. The data reveals a linear trend when investigated, so by fitting a linear model we tried to remove the trend and, then we used the trend-free data for further calculations. Four theoretical semivariogram models Exponential, Spherical, Gaussian and Matern were used to explain the spatial variation among the average annual rainfall. These models are chosen according to the pattern of empirical semivariogram. The prediction performance of Ordinary kriging with these four fitted models are then compared through ����-fold cross-validation and it is found that Ordinary Kriging performs better when the spatial dependency in average annual rainfall of Bangladesh is modeled through Gaussian semivariogram model.

並列關鍵字

Geostatistics ； Semivariogram ； Spatial Interpolation ； Ordinary Kriging ； Cross-validation ； Rainfall