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

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  • Youngmin Seo
  • Sungwon Kim
  • Vijay Singh

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

Suggested Citation

  • Youngmin Seo & Sungwon Kim & Vijay Singh, 2015. "Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2189-2204, May.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:7:p:2189-2204
    DOI: 10.1007/s11269-015-0935-9
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    References listed on IDEAS

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    1. Sungwon Kim & Jalal Shiri & Ozgur Kisi & Vijay Singh, 2013. "Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2267-2286, May.
    2. Bergmeir, Christoph & Benítez, José M., 2012. "Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i07).
    3. Kostas Moustris & Ioanna Larissi & Panagiotis Nastos & Athanasios Paliatsos, 2011. "Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(8), pages 1979-1993, June.
    4. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    5. Sungwon Kim & Jalal Shiri & Ozgur Kisi, 2012. "Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3231-3249, September.
    6. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
    7. Ioannis Trichakis & Ioannis Nikolos & G. Karatzas, 2011. "Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1143-1152, March.
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    Cited by:

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    2. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    3. A. Tavassoli & Y. Waghei & A. Nazemi, 2022. "Hybrid MLP-IDW approach based on nearest neighbor for spatial prediction," Computational Statistics, Springer, vol. 37(4), pages 1943-1962, September.
    4. Peyman Abbaszadeh, 2016. "Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1701-1721, March.
    5. Morteza Pakdaman & Iman Babaeian & Zohreh Javanshiri & Yashar Falamarzi, 2022. "European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 611-623, January.

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