Attention-Guided Position-Sensitive Multiple Imputation for Wastewater Treatment Process

Abstract

Missing values frequently appearing in the wastewater treatment process are automatically replaced by zero to ensure the implementation of downstream applications. These meaningless zero values bias data distribution and decrease data quality. However, the existing imputation methods treat all values equally without considering the existence of meaningless zero values, affecting the performances of imputation and downstream models. Thus, an attention-guided position-sensitive multiple imputation (APMI) method is proposed. First, a position-sensitive localization attention module selectively focuses on the most informative values, enhancing the ability for observed data utilization. Second, a masked attention multiple imputation module focuses on the observed values and fuses multiple candidate estimations as the final result to improve imputation performance. Third, a joint optimization objective function is designed to ensure the consistency of localization and imputation tasks. The extensive experimental results show that the proposed APMI outperforms existing method imputation performance under different missing rates.