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Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

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  • Sungwon Kim
  • Vijay Singh
  • Youngmin Seo
  • Hung Kim

Abstract

The objective of this study is to develop soft computing and data reconstruction techniques for modeling monthly California Irrigation Management Information System (CIMIS) evapotranspiration (ET o ) at two stations, U.C. Riverside and Durham, in California. The nonlinear dynamics of monthly CIMIS ET o is examined using autocorrelation function, phase space reconstruction, and close returns plot. The generalized regression neural networks and genetic algorithm (GRNN-GA) conjunction model is developed for modeling monthly CIMIS ET o . Among different input variables considered, solar radiation (RAD) is found to be the most effective variable for modeling monthly CIMIS ET o using GRNN-GA for both stations. Adding other input variables to the best 1-input combination improves the model performance. The generalized regression neural networks and backpropagation algorithm (GRNN-BP) conjunction model is compared with the results of GRNN-GA for modeling monthly CIMIS ET o . Two bootstrap resampling methods are implemented to reconstruct the training data. Method 1 (1-BGRNN-GA) employs simple extensions of training data using the bootstrap resampling method. For each training data, method 2 (2-BGRNN-GA) uses individual bootstrap resampling of original training data. Results indicate that Method 2 (2-BGRNN-GA) improves modeling of monthly CIMIS ET o and is more stable and reliable than are GRNN-GA, GRNN-BP, and Method 1 (1-BGRNN-GA). Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Sungwon Kim & Vijay Singh & Youngmin Seo & Hung Kim, 2014. "Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 185-206, January.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:1:p:185-206
    DOI: 10.1007/s11269-013-0479-9
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    References listed on IDEAS

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    1. 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.
    2. Gilmore, Claire G., 1993. "A new test for chaos," Journal of Economic Behavior & Organization, Elsevier, vol. 22(2), pages 209-237, October.
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    Cited by:

    1. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    2. Mohammad Valipour, 2014. "Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4237-4255, September.
    3. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.

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