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Developing Stepwise m5 Tree Model to Determine the Influential Factors on Rainfall Prediction and to Overcome the Greedy Problem of its Algorithm

Author

Listed:
  • Khalil Ghorbani

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Meysam Salarijazi

    (Gorgan University of Agricultural Sciences and Natural Resources)

  • Nozar Ghahreman

    (University of Tehran)

Abstract

Large scale climatic phenomenon with lag time may be used as essential variables for stepwise prediction of rainfall, but the interaction of these signals on the occurrence of rainfall leads to non-linear, complex nature of relations. A model tree is a promising tool for modeling complex systems and recognizing the most significant variables. The model tree approach uses a greedy algorithm in which the increased number of variables does not necessarily improve the model's accuracy; hence the model should be run stepwise. This study employed a stepwise M5 model tree to predict annual rainfall in Hashem Abad station, north of Iran, using observational data and 17 climatic signals during the 1985–2019 period to determine the most significant variables. For this purpose, 131,017 subsets consisting of 17 members were produced, and the M5 model tree was fitted on each of them. The best combination of variables with the highest accuracy simulated the rainfall with a 36-mm error (less them 5–6%) and a correlation coefficient of 94%. Among the climatic signals, the Sun Spot (SP) was placed in the tree root (most significant), while the Nino 4, EA, and NAO were ranked as the other significant predictors, respectively. The results also indicated that due to the nature of rainfall variations and the greedy algorithm of the M5 model, it is necessary to perform stepwise modeling. The lag time in teleconnections may be considered a suitable feature for early prediction of next year's rainfall and capturing the inter-annual variation.

Suggested Citation

  • Khalil Ghorbani & Meysam Salarijazi & Nozar Ghahreman, 2022. "Developing Stepwise m5 Tree Model to Determine the Influential Factors on Rainfall Prediction and to Overcome the Greedy Problem of its Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3327-3348, July.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03203-3
    DOI: 10.1007/s11269-022-03203-3
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    References listed on IDEAS

    as
    1. Ali Rahimikhoob, 2016. "Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3063-3075, July.
    2. Gabriel Gómez-Martínez & Miguel A. Pérez-Martín & Teodoro Estrela-Monreal & Patricia del-Amo, 2018. "North Atlantic Oscillation as a Cause of the Hydrological Changes in the Mediterranean (Júcar River, Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2717-2734, June.
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    Cited by:

    1. Vanita Jain & Aarushi Dhingra & Eeshita Gupta & Ish Takkar & Rachna Jain & Sardar M. N. Islam, 2023. "Influence of Land Surface Temperature and Rainfall on Surface Water Change: An Innovative Machine Learning Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3013-3035, June.
    2. Mohammad Ehteram & Ali Najah Ahmed & Zohreh Sheikh Khozani & Ahmed El-Shafie, 2023. "Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3631-3655, July.

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    More about this item

    Keywords

    Greedy problem; Rainfall prediction; Stepwise M5 tree; Sunspot; Teleconnection;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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