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Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models

Author

Listed:
  • Mohammad Taghi Sattari

    (University of Tabriz
    Ankara University)

  • Fatemeh Shaker Sureh

    (University of Tabriz)

  • Ercan Kahya

    (Istanbul Technical University)

Abstract

The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean Oscillation-Algiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results.

Suggested Citation

  • Mohammad Taghi Sattari & Fatemeh Shaker Sureh & Ercan Kahya, 2020. "Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1077-1094, July.
    • Handle: RePEc:spr:nathaz:v:102:y:2020:i:3:d:10.1007_s11069-020-03946-5
    DOI: 10.1007/s11069-020-03946-5
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    References listed on IDEAS

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    1. Tsegaye Tadesse & Donald Wilhite & Sherri Harms & Michael Hayes & Steve Goddard, 2004. "Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 33(1), pages 137-159, September.
    2. I. Fustos & R. Abarca-del-Rio & P. Moreno-Yaeger & M. Somos-Valenzuela, 2020. "Rainfall-Induced Landslides forecast using local precipitation and global climate indexes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 115-131, May.
    3. Vahid Nourani & Mohammad Taghi Sattari & Amir Molajou, 2017. "Threshold-Based Hybrid Data Mining Method for Long-Term Maximum Precipitation Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2645-2658, July.
    4. Juan Antonio Luque-Espinar & Rosa María Mateos & Inmaculada García-Moreno & Eulogio Pardo-Igúzquiza & Gerardo Herrera, 2017. "Spectral analysis of climate cycles to predict rainfall induced landslides in the western Mediterranean (Majorca, Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 985-1007, December.
    5. Lamine Diop & Saeed Samadianfard & Ansoumana Bodian & Zaher Mundher Yaseen & Mohammad Ali Ghorbani & Hana Salimi, 2020. "Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 733-746, January.
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    More about this item

    Keywords

    Monthly precipitation; Atmospheric circulations; Meteorological variables; Random forest; M5 tree model; Iran;
    All these keywords.

    JEL classification:

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

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