标题： AQFusionNet：利用图像和传感器数据进行空气质量指数预测的多模态深度学习 显示英文标题

标题： AQFusionNet: Multimodal Deep Learning for Air Quality Index Prediction with Imagery and Sensor Data

摘要： 空气质量监测在资源受限地区仍然具有挑战性，这是由于传感器部署稀疏和基础设施有限。 这项工作介绍了AQFusionNet，这是一种用于鲁棒空气质量指数（AQI）预测的多模态深度学习框架。 该框架使用轻量级CNN主干网络（MobileNetV2、ResNet18、EfficientNet-B0）将地面大气图像与污染物浓度数据相结合。 通过语义对齐的嵌入空间结合视觉和传感器特征，实现了准确且高效的预测。 在来自印度和尼泊尔的超过8000个样本上的实验表明，AQFusionNet始终优于单模态基线，使用EfficientNet-B0主干网络实现了高达92.02%的分类准确率和7.70的RMSE。 该模型在保持低计算开销的同时，比单模态方法提高了18.5%，使其适合在边缘设备上部署。 AQFusionNet为基础设施有限环境中的AQI监测提供了一个可扩展且实用的解决方案，在部分传感器可用的情况下也能提供稳健的预测能力。 显示英文摘要

摘要： Air pollution monitoring in resource-constrained regions remains challenging due to sparse sensor deployment and limited infrastructure. This work introduces AQFusionNet, a multimodal deep learning framework for robust Air Quality Index (AQI) prediction. The framework integrates ground-level atmospheric imagery with pollutant concentration data using lightweight CNN backbones (MobileNetV2, ResNet18, EfficientNet-B0). Visual and sensor features are combined through semantically aligned embedding spaces, enabling accurate and efficient prediction. Experiments on more than 8,000 samples from India and Nepal demonstrate that AQFusionNet consistently outperforms unimodal baselines, achieving up to 92.02% classification accuracy and an RMSE of 7.70 with the EfficientNet-B0 backbone. The model delivers an 18.5% improvement over single-modality approaches while maintaining low computational overhead, making it suitable for deployment on edge devices. AQFusionNet provides a scalable and practical solution for AQI monitoring in infrastructure-limited environments, offering robust predictive capability even under partial sensor availability.