Forest ecological long sequence missing data imputation method based on BiP-Informer

Abstract

The monitoring of forest ecological data primarily relies on various types of sensors. However, long sequence of data are often missing. The commonly used long sequence missing data imputation algorithms suffer from problems such as error accumulation and lack of long-term dependencies, making it difficult to achieve accurate missing data imputation. To address this issue, this paper proposes a deep learning model dubbed BiP-Informer (Bidirectional Penalty-Informer). BiP-Informer is founded on the sparse self-attention mechanism, which adds the bidirectional information transmission mechanism to effectively capture the long-term dependencies. The bidirectional loss penalty strategy is proposed to alleviate error accumulation. The experimental results shows that the BiP-Informer outperformed commonly used methods by 25–52%, achieving the best imputation results with MAE of 0.97%, RMSE of 1.56%, and R2 of 99.25%. The BiP-Informer not only provides a new solution for sensor monitoring missing data imputation in forest areas but also has widespread application prospects.