Assessment of Different Methods for Estimation of Missing Rainfall Data

Tuğçe Hırca () and Gökçen Eryılmaz Türkkan ()
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Tuğçe Hırca: Bayburt University
Gökçen Eryılmaz Türkkan: Balıkesir University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 15, No 6, 5945-5972

Abstract: 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.

Keywords: Susurluk basin; Missing rainfall data; Missing data pattern; Missing data mechanism; Expectation–maximization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03936-3

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