Evaluation of missing value imputation methods for wireless soil datasets

AbstractSoil data are very important for hydrologists to model and predict the evolution of water–soil environments. In present, the soil data are often collected by unattended wireless sensing system and then inevitably involves continuous missing values due to the unreliability of system, which is different from the manually collected datasets with the data losses being sparsely distributed . This paper investigates seven typical methods that are used to infill soil missing data, and in particular we also attempt to employ the extreme learning machine in missing-data infilling. This work is aimed at answering such a question: Whether or not existing methods suit for wireless sensory soil dataset with continuous missing values, and how well they perform. With a real-world soil dataset involving complete samples as the benchmark, we evaluate and compare these methods , and analyze the possible reasons behind. This study provides insights for designing new methods that can effectively deal with the missing values in wireless sensory soil dataset.

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