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Novel Methods for Imputing Missing Values in Water Level Monitoring Data

Author

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  • Thakolpat Khampuengson

    (University of East Anglia
    Hydro-Informatics Institute of Ministry of Higher Education)

  • Wenjia Wang

    (University of East Anglia)

Abstract

Hydrological data are collected automatically from remote water level monitoring stations and then transmitted to the national water management centre via telemetry system. However, the data received at the centre can be incomplete or anomalous due to some issues with the instruments such as power and sensor failures. Usually, the detected anomalies or missing data are just simply eliminated from the data, which could lead to inaccurate analysis or even false alarms. Therefore, it is very helpful to identify missing values and correct them as accurate as possible. In this paper, we introduced a new approach - Full Subsequence Matching (FSM), for imputing missing values in telemetry water level data. The FSM firstly identifies a sequence of missing values and replaces them with some constant values to create a dummy complete sequence. Then, searching for the most similar subsequence from the historical data. Finally, the identified subsequence will be adapted to fit the missing part based on their similarity. The imputation accuracy of the FSM was evaluated with telemetry water level data and compared to some well-established methods - Interpolation, k-NN, MissForest, and also a leading deep learning method - the Long Short-Term Memory (LSTM) technique. Experimental results show that the FSM technique can produce more precise imputations, particularly for those with strong periodic patterns.

Suggested Citation

  • Thakolpat Khampuengson & Wenjia Wang, 2023. "Novel Methods for Imputing Missing Values in Water Level Monitoring Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 851-878, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03408-6
    DOI: 10.1007/s11269-022-03408-6
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    References listed on IDEAS

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    1. Wai Yan Lai & K. K. Kuok, 2019. "A Study on Bayesian Principal Component Analysis for Addressing Missing Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2615-2628, June.
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