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

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• 作者：Youngmin Seo ; Sungwon Kim ; Vijay P. Singh
• 关键词：Spatial precipitation estimation ; Geostatistical interpolation ; Regression kriging ; Neural network residual kriging ; Spatial random sampling
• 刊名：Water Resources Management
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：29
• 期：7
• 页码：2189-2204
• 全文大小：2,742 KB
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• 作者单位：Youngmin Seo (1)
  Sungwon Kim (2)
  Vijay P. Singh (3)
  
  1. Department of Constructional Disaster Prevention Engineering, Kyungpook National University, Sangju, 742-711, South Korea
  2. Department of Railroad and Civil Engineering, Dongyang University, Yeongju, 750-711, South Korea
  3. Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, 77843-2117, TX, USA
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

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.