Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach

Youngmin Seo (), Sungwon Kim () and Vijay Singh ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2015, vol. 29, issue 7, 2189-2204

Abstract: A hybrid model, combining regression kriging and neural network residual kriging (RKNNRK), is developed for determining spatial precipitation distribution. The RKNNRK model is compared with current spatial interpolation models, including simple kriging (SK), ordinary kriging (OK), universal kriging (UK), regression kriging (RK) and neural network residual kriging (NNRK). Results show that hybrid models, including RK, NNRK and RKNNRK, performed better than SK, OK and UK, based on the coefficient of efficiency (CE), coefficient of determination (r 2 ), index of agreement (d), mean squared relative error (MSRE), mean absolute error (MAE), root-mean-square error (RMSE), and mean squared error (MSE). Of the six spatial interpolation models, the RKNNRK model was the most accurate, and the NNRK model was the second best. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Spatial precipitation estimation; Geostatistical interpolation; Regression kriging; Neural network residual kriging; Spatial random sampling (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11269-015-0935-9

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