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Assessment of Different Methods for Estimation of Missing Rainfall Data

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  • Tuğçe Hırca

    (Bayburt University)

  • Gökçen Eryılmaz Türkkan

    (Balıkesir University)

Abstract

Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

Suggested Citation

  • Tuğçe Hırca & Gökçen Eryılmaz Türkkan, 2024. "Assessment of Different Methods for Estimation of Missing Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 5945-5972, December.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:15:d:10.1007_s11269-024-03936-3
    DOI: 10.1007/s11269-024-03936-3
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    References listed on IDEAS

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    1. Okba Weslati & Moncef Bouaziz & Mohamed-Moncef Serbaji, 2023. "Precipitation Forecasting and Monitoring in Degraded Land: A Study Case in Zaghouan," Land, MDPI, vol. 12(4), pages 1-18, March.
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    4. Reza Modarres, 2008. "Regional Frequency Distribution Type of Low Flow in North of Iran by L-moments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(7), pages 823-841, July.
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