# Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

## TL;DR

A new model called GA-BiLSTM improves the accuracy of filling missing data in urban environmental monitoring systems, especially during long sensor outages.

## Contribution

The novel GA-BiLSTM model outperforms existing methods in imputing missing urban sensor data by leveraging spatiotemporal dependencies.

## Key findings

- GA-BiLSTM outperformed XGBoost and K-nearest neighbors in imputing missing data during both short and long sensor outages.
- Peripheral rural sensor nodes were found to play a critical role in predicting urban sensor data.
- The model's performance highlights the importance of advanced imputation for reliable urban environmental monitoring.

## Abstract

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.