Deep Learning-Based Multi-Modal Sensor Fusion for Real-Time Environmental Monitoring and Intelligent Decision Making

Xiaolei Zhong

doi:10.63313/AJET.9021

Authors

Xiaolei Zhong Dianchi College, Kunming 650228, China Author

DOI:

https://doi.org/10.63313/AJET.9021

Keywords:

Multi-modal sensor fusion, Deep learning, Environmental monitoring, Real-time processing, Intelligent systems, Graph neural networks

Abstract

Environmental monitoring systems require sophisticated integration of heterogeneous sensor data to provide accurate real-time assessment and intelligent decision support. This paper presents a novel deep learning framework that combines multi-modal sensor fusion with advanced image processing techniques for comprehensive environmental monitoring. Our approach leverages a hybrid neural architecture incorporating Transformer-based attention mechanisms and Graph Convolutional Networks (GCN) to process data from various sensors including LiDAR, hyperspectral cameras, air quality sensors, and meteorological stations. The proposed Multi-Modal Environmental Fusion Network (MEFN) achieves superior performance with 96.7% accuracy in environmental anomaly detection and 89.3% precision in predictive analytics compared to existing methods. Experimental validation across three different environmental settings demonstrates the system's robustness and scalability for large-scale deployment with a total project budget of $40,000.

References

[1] Zhu Y ,Yang D ,Lee Y .Deformable and Fragile Object Manipulation: A Review and Pro-spects[J].Sensors,2025,25(17):5430-5430.DOI:10.3390/S25175430.

[2] Zhao M ,Taal C ,Baggerohr S , et al.Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics[J].Mechanical Systems and Signal Processing,2025,230112544-112544.DOI:10.1016/J.YMSSP.2025.112544.

[3] Alisha M ,Anirudh N ,Reva A , et al.Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata.[J].Brain infor-matics,2022,9(1):14-14.DOI:10.1186/S40708-022-00162-8.

[4] Xian Y ,Zhang D ,Wang X , et al.A dual-branch network based on optical flow learning and semantic consistency for macro-expression spotting[J].Applied Intelli-gence,2024,(prepublish):1-16.DOI:10.1007/S10489-024-05726-1.

[5] Min Y ,Li J ,Jia S , et al.Automated Cerebrovascular Segmentation and Visualization of In-tracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learn-ing.[J].Journal of imaging informatics in medi-cine,2024,38(2):1-14.DOI:10.1007/S10278-024-01215-6.

[6] Wu H ,Sawada T ,Goto T , et al.Edge AI Model Deployed for Real-Time Detection of Atrial Fibrillation Risk during Sinus Rhythm.[J].Journal of clinical medi-cine,2024,13(8):DOI:10.3390/JCM13082218.

[7] Li H ,Shafieezadeh M M .Smart sensor architecture selection for coastal marine monitor-ing[J].Applied Water Science,2025,15(12):302-302.DOI:10.1007/S13201-025-02664-2.

[8] Stephen C ,Baiqian S ,Miao W , et al.A Low-Cost Radar-Based IoT Sensor for Noncontact Measurements of Water Surface Velocity and Depth.[J].Sensors (Basel, Switzer-land),2023,23(14):DOI:10.3390/S23146314.

[9] Ramadan N M ,Ali A M ,Khoo Y S , et al.AI-powered IoT and UAV systems for real-time de-tection and prevention of illegal logging[J].Results in Engineer-ing,2024,24103277-103277.DOI:10.1016/J.RINENG.2024.103277.

[10] Liu C ,Deng B ,Ye X , et al.Intelligent decision making under conflicting and cooperative scenarios with incomplete information[J].Applied Soft Compu-ting,2025,173112898-112898.DOI:10.1016/J.ASOC.2025.112898.