Missing Value Imputation for Wireless Sensory Soil Data: A Comparative Study

Soil data is indispensable in hydrology or other environmental sciences. In present, the soil data is often collected by unattended wireless sensing system and then inevitably involves continuous missing values due to the unreliability of system, while in traditional commercial or manually-collected datasets, the data losses are sparsely distributed. time-series dataset, 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 two-month complete samples as the benchmark, we evaluate these six missing value infilling methods, compare their performance, 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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