DynamicSeq2SeqXGB for PM2.5 imputation in extremely sparse environmental monitoring networks.

ABSTRACT: Environmental monitoring networks face critical data gaps that compromise public health protection and regulatory compliance, with missing data rates often exceeding 40% in operational settings. This study validates DynamicSeq2SeqXGB, a novel hybrid model that integrates a sequence-to-sequence encoder-decoder for temporal pattern extraction with an XGBoost regressor for robust gap reconstruction under extreme sparsity. Data from five monitoring stations in Pavlodar, Kazakhstan, collected over a 15-month period from May 23, 2024 to July 19, 2025, were analyzed representing severely compromised infrastructure (completeness rates 23.3-57.5%). The methodology employs adaptive context processing and implements hierarchical decomposition for extended outages. Two data preparation strategies were

SUBMITTER: Safarov R 

PROVIDER: S-EPMC12697988 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature