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Threshold-Based Hybrid Data Mining Method for Long-Term Maximum Precipitation Forecasting

Author

Listed:
  • Vahid Nourani

    (University of Tabriz
    Near East University)

  • Mohammad Taghi Sattari

    (University of Tabriz)

  • Amir Molajou

    (University of Tabriz)

Abstract

In this paper, the application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between several thresholds of monthly precipitation (MP) values of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend sea surface temperature (SST) of the Black, Mediterranean and Red Seas. Two major steps of the modeling in this study were the classification of de-trend SST data and selecting the most effective groups and extracting hidden predictive information involved in the data. The decision tree techniques which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and lift measures were calculated and compared for different thresholds of precipitation at different lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast extreme precipitation events considering higher threshold values and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and maximum MP of Tabriz and Kermanshah synoptic stations so that the confidence between the threshold of 35% of MP values and the de-trend SST of seas is higher than 70 for Tabriz and 60% for Kermanshah. It was also shown that the geographical location of stations and the distribution of precipitation data affect the measures of the rules and forecasting outcomes.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:9:d:10.1007_s11269-017-1649-y
    DOI: 10.1007/s11269-017-1649-y
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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. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
    3. Seyed Akrami & Vahid Nourani & S. Hakim, 2014. "Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2999-3018, August.
    4. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
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    Cited by:

    1. Mohammad Taghi Sattari & Anca Avram & Halit Apaydin & Oliviu Matei, 2020. "Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models," Mathematics, MDPI, vol. 8(9), pages 1-21, August.
    2. 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.
    3. Amir Molajou & Parsa Pouladi & Abbas Afshar, 2021. "Incorporating Social System into Water-Food-Energy Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4561-4580, October.

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