Real-time Air Quality Prediction System: Practice of Building an LSTM-based AQI Early Warning Platform

This article introduces an end-to-end machine learning system that uses LSTM neural networks to achieve real-time prediction of the Air Quality Index (AQI), and builds REST API services and an interactive dashboard to provide health warnings. The system covers the entire link from data collection, model training to service deployment, aiming to help the public (especially sensitive groups) respond to air quality issues in a timely manner.

Air quality issues directly affect health and quality of life, and sensitive groups have an urgent need for AQI early warnings. This system is a complete solution integrating data collection, model prediction, service deployment, and visual display. Its core capabilities include real-time data collection, AQI trend prediction, health recommendation generation, API services, and interactive display. The technology stack covers data engineering, machine learning, back-end development, and front-end visualization, realizing the complete link from raw data to user value.

AQI prediction is a time-series problem. Influencing factors include meteorological conditions (temperature, humidity, etc.), pollutant concentrations (PM2.5, etc.), geographical location, and seasonal characteristics. Data is sourced from public air quality monitoring APIs and meteorological service APIs, which need to undergo cleaning and preprocessing (handling missing/anomalous values). Feature engineering constructs derived features: lag features (AQI over the past few hours), sliding window statistics (24-hour average/maximum values), and time features (hour/week/holiday) to capture periodicity and trends.

LSTM solves the gradient vanishing problem through a gating mechanism, making it suitable for capturing long-term trends and short-term fluctuations of AQI. The model uses multi-layer LSTM stacking + Dropout to prevent overfitting; the input window length is 24-72 hours (balancing information and computation); the output layer is designed according to the target (single-step prediction outputs a scalar, multi-step prediction outputs a vector). Training requires dividing the dataset in chronological order to avoid leakage, and uses time-series cross-validation for evaluation.

Deploying the model as a REST API is key to realizing its value. Core endpoints include getting current AQI, future predictions, health recommendations, and historical data queries. The service architecture considers performance (asynchronous inference, message queue + worker scaling) and reliability (version management supporting canary release/rollback). Security requires implementing API Key authentication, rate limiting, input validation, and HTTPS encryption.

The dashboard serves as the user interface, containing visual elements such as real-time AQI values/levels, trend charts, prediction curves, pollutant concentrations, and station distributions. The technology stack selected is React/Vue + ECharts; real-time updates are achieved via WebSocket/long polling, and mobile adaptation is supported. Health warnings provide recommendations based on predicted AQI levels (excellent/good/polluted, etc.), such as reminding sensitive groups to reduce outdoor activities when AQI exceeds 150.

Deployment involves components such as databases (storing historical data), caching (accelerating hot access), model services, web services, and task scheduling (scheduled updates/re-training). Docker containerization and K8s orchestration can be used to simplify the process. Operation and maintenance need to monitor API response time/error rate, model latency, record key logs; and detect model drift to trigger re-training in a timely manner.

This system realizes the transformation of machine learning technology into public services, with each link carefully designed to help the public deal with air quality issues. Future explorations can include: introducing satellite remote sensing/traffic flow data to improve accuracy; developing personalized warnings (based on user health/location); and trying models like Transformer/GNN to capture spatio-temporal dependencies.