Multi-sensor data fusion technology for the early landslide warning system

• Published in: Journal of ambient intelligence and humanized computing 2023-08, Vol.14 (8), p.11165-11172
• Main Authors: Chen, Musheng, Cai, Zhishan, Zeng, Yongxi, Yu, Yanzhong
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Basic converters; Computational Intelligence; Data integration; Decision making; Decision theory; Emergency communications systems; Emergency warning programs; Engineering; Fuzzy sets; Global navigation satellite system; Internet of Things; Landslides; Landslides & mudslides; Monitoring systems; Multisensor fusion; Original Research; Rainfall; Robotics and Automation; Sensors; User Interfaces and Human Computer Interaction; Warning systems
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-022-04396-6

The triggering threshold is one of the most important methods for early warning of landslide disasters. And the traditional method is to conduct a simple comparative analysis of the data collected at each monitoring point, which cannot take full advantage of the information provided by the data. In order to overcome this setback and improve the accuracy of the early warning, rainfall detector, global navigation satellite system and deep displacement sensor are used to detect external factors and internal states that cause landslides. Then, on this basis, a new data fusion technology based on the generalized evidence theory is proposed in this paper. Firstly, the system collects information from different sensors and transmits them to the landslide probability. Considering that the landslide is a gradual process, a new method is used to convert the landslide probability into the basic probability assignment for intuitionistic fuzzy sets. Then, the fuzzy divergence measure is used to calculate the uncertainly of evidence, which expresses the relative importance of the evidence. Next, the ultimate weight of each sensor is applied to adjust the mass function and gain the reliability. Finally, the system makes a final decision according to fusion results based on the generalized evidence theory. Three types of multi-sensors data were used to test the performance of the proposed algorithm. Comparing with other four fusion method, the proposed method has a better performance, which can increase the basic probability assignments from 0.73 to 0.92. Moreover, the proposed model achieved when dealing data with high degree of conflict. The simulation results showed that the proposed method could reduce the uncertainly and get a more comprehensive and integrated decision for the early landslide warning system.