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Predicting Infiltration Using Kernel Extreme Learning Machine Model Under Input and Parameter Uncertainty

In: Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author

Listed:
  • Mohammad Ehteram

    (Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering)

  • Akram Seifi

    (Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture)

  • Fatemeh Barzegari Banadkooki

    (Payame Noor University, Agricultural Department)

Abstract

This study develops the optimized kernel extreme learning machines (KELMs) for predicting the infiltration rate. The rat swarm optimization algorithm (RSOA), shark optimization (SO), and dragonfly algorithm (DRA) were used to find the KELM parameters. This study also used generalized likelihood uncertainty estimation (GLUE) for quantifying input and parameter uncertainties. The furrow length had the highest importance among other input parameters. Also, the KELM-RSOA outperformed the other models. The MAE of the KELM-RSOA, KEML-SO, KELM-DRA, and KELM models was 0.02, 0.05, 0.07, and 0.10 at the training level. The MAE of the KELM-RSOA, KEML-SO, KELM-DRA, and KELM models was 0.04, 0.08, 0.10, and 0.12 at the testing level. The results revealed that the model parameters provided higher uncertainty than the input parameters.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Predicting Infiltration Using Kernel Extreme Learning Machine Model Under Input and Parameter Uncertainty," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 147-162, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_15
    DOI: 10.1007/978-981-19-9733-4_15
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