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

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Abstract

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.

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References

  • Chen X, Li J, Zhang Y, Tao L (2016) Change detection with multi-source defective remote sensing images based on evidential fusion. Ann Photogramm, Remote Sens Spatial Inf Sci 7:125

    Article  Google Scholar 

  • Chen X, Li J, Zhang Y, Jiang W (2017) Evidential fusion based technique for detecting landslide barrier lakes from cloud-covered remote sensing images. IEEE J-STARS 99:1–16

    Google Scholar 

  • Deng Y (2015) Generalized evidence theory. Appl Intell 43:530–543

    Article  Google Scholar 

  • Deng Y (2016) Deng entropy. Chaos Soliton Fract 91:549–553

    Article  MATH  Google Scholar 

  • Ding Q, Peng X, Liu T, Tong Q (2014) Building fire alarm system with multi-sensor and information fusion technology based on D-S evidence theory. Algorithms 7(4):906–909

    Article  Google Scholar 

  • Gamperl M, Singer J, Thuro K (2021) Internet of things geosensor network forcost-effective landslide early warning systems. Sensors 21(8):2609

    Article  Google Scholar 

  • Gan D, Yang B, Tang Y (2019) An extended base belief function in Dempster Shafer evidence theory and its application in conflict data fusion. IEEE Access 7:3928–3937

    Google Scholar 

  • Gong Z, Cao Y (2020) Intuitionistic fuzzy divergence measure based on X2 distribution and its application in the recommendation system. J Northwest Norm Univ 56(1):10–20

    MATH  Google Scholar 

  • Hemalatha T, Ramesh M, Rangan V (2019) Effective and accelerated forewarning of landslides using wireless sensor networks and machine learning. IEEE Sens J 2019(21):9964–9975

    Article  Google Scholar 

  • Jiang W, Wei B, Xie C, Zhou D (2016) An evidential sensor fusion method in fault diagnosis. Adv Mech Eng 8(3):1–7

    Article  Google Scholar 

  • Ling Q, Zhang Q, Zhang J, Kong L, Zhang W, Zhu L (2021) Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi. China Nat Hazards 108:925–946

    Article  Google Scholar 

  • Liu J, Tang H, Li Q, Su A, Liu Q, Zhong C (2018) Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China). Geomat Na Haz Risk 9(1):881–891

    Article  Google Scholar 

  • Liu C, Shao X, Li W (2019) Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring. Geomat Na Haz Risk 10(1):151–167

    Article  Google Scholar 

  • Liu Y, Xu C, Huang B, Ren X, Liu C, Hu B, Chen Z (2020) Landslide displacement prediction based on multi-source data fusion and sensitivity states. Eng Geol 271:105608

    Article  Google Scholar 

  • Liu Y, Xu C, Huang B, Ren X, Liu C, Hu B, Chen Z (2021) Landslide displacement prediction based on variational mode decomposition and MIC-GWO-LSTM model. Research square 5:339491

    Google Scholar 

  • Ma L, You Y, Miao Q (2008) The research of landslide forecast reduced by strong precipitation. J MT Sci 26(5):583–589

    Google Scholar 

  • Naidu S, Sajinkumar K, Oommen T, Anuja V, Samuel R, Muraleedharan C (2018) Early warning system for shallow landslides using rainfall threshold and slope stability analysis. Geosci Front 9(6):1871–1882

    Article  Google Scholar 

  • Park J, Lee S, Kim Y, Kang S, Lee D (2020) A Regional-scale landslide early warning system based on the sequential evaluation method: development and performance analysis. Appl Sci 10(17):5788

    Article  Google Scholar 

  • Prabha R, Ramesh M, Rangan V, Ushakumari P, Hemalatha T (2017) Energy efficient data acquisition techniques using context aware sensing for landslide monitoring systems. IEEE Sens J 17(18):6006–6017

    Article  Google Scholar 

  • Purnomo F, Yoeseph N, Abisatya G (2019) Landslide early warning system based on arduino with soil movement and humidity sensors. J Phys: Conf Ser 1153(1):012034

    Google Scholar 

  • Rao Y, Zhang X, Li J, Zhen L, Wang D (2017) Relationship between slope safety factor and landslide probability. J Yangtze River Sci Res Inst 34(5):63–67

    Google Scholar 

  • Reyana A, Vijayalakshmi P (2021) Multi-sensor data fusion technique for energy conservation in the wireless sensor network application condition-based environment monitoring. J Ambient Intell Hum Comput 13:1–10

    Google Scholar 

  • Rodriguez J, Deane E, Hendry M, Macciotta R, Evans T, Grpel C, Skirro R (2021) Practical evaluation of single-frequency dGNSS for monitoring slow-moving landslides. Landslides 18:3671–3684

    Article  Google Scholar 

  • Rosi A, Segoni S, Canavesi V, Monni A, Casagli N (2020) Definition of 3D rainfall thresholds to increase operative landslide early warning system performances. Landslides 18(4):01523

    Google Scholar 

  • Sameen M, Pradhan B (2019) Landslide detection using residual networks and the fusion of spectral and topographic information. IEEE Access 99:2935761

    Google Scholar 

  • Sindhu S, Saravanan M (2021). Architectural framework for multi sensor data fusion and validation in IoT based system. In: 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 448–452

  • Tan Q, Wang P, Hu J, Zhou P, Bai M, Hu J (2020) The application of multi-xensor target tracking and fusion technology to the comprehensive early warning information extraction of landslide multi-point monitoring data. Measurement 166:108044

    Article  Google Scholar 

  • Tang Y, Zhou D, Chan F (2018) An extension to Deng’s entropy in the open world assumption with an application in sensor data fusion. Sensors 18(6):1902–1917

    Article  Google Scholar 

  • Xiao F (2020) A new divergence measure for belief functions in D-S evidence theory for multi-sensor data fusion. Inf Sci 514:462–483

    Article  MATH  Google Scholar 

  • Zhang J (2012) Evaluation method of slope stability based on displacement ratio. J Guizhou Univ (Nat Sci) 29(6):122–125

    Google Scholar 

  • Zhang H, Deng Y (2019) Weighted belief function of sensor data fusion in engine fault diagnosis. Soft Comput 24(7):2329–2339

    Google Scholar 

  • Zhang J, Huang S (2009) Relationship between landslide displacement and precipitation. J Geomatics 34(3):27–29

    Google Scholar 

  • Zhang Y, Wang L, Shu B, He S, Li L (2020) Application of an adaptive weighted estimation fusion algorithm in landslide deformation monitoring data processing. IOP Conf Ser Earth Environ Sci 570(6):062045

    Article  Google Scholar 

  • Zhi L, Kanai K, Takeuchi M, Tsuda T, Watanabe H (2017). Adaptive video streaming in hybrid landslide detection system with D-S theory. In: IEEE International Conference on Smart Computing. IEEE, 7946984

Download references

Acknowledgements

The work is partially supported by the innovation foundation for Doctor Dissertation of Quanzhou Normal University (H19026), science and technology project of Quanzhou (2018Z031, 2019N115S) and Natural Science Foundation of Fujian Province (2019J01736).

Funding

Natural Science Foundation of Fujian Province, 2019J01736, Musheng Chen, science and technology project of Quanzhou, 2018Z031, Musheng Chen, 2019N115S, Yongxi Zeng.

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Authors and Affiliations

  1. College of Physics and Information Engineering, Quanzhou Normal University, Quanzhou, China

    Musheng Chen, Zhishan Cai, Yongxi Zeng & Yanzhong Yu

  2. Fujian Provincial Key Laboratory of Functional Materials for Informatics, Fujian, Quanzhou, China

    Musheng Chen, Zhishan Cai, Yongxi Zeng & Yanzhong Yu

  3. Fujian Provincial Key Laboratory for Advanced Micro-Nano Photonics Technology and Devices, Fujian, Quanzhou, China

    Musheng Chen, Zhishan Cai, Yongxi Zeng & Yanzhong Yu

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  1. Musheng Chen
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  2. Zhishan Cai
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Correspondence to Zhishan Cai.

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Chen, M., Cai, Z., Zeng, Y. et al. Multi-sensor data fusion technology for the early landslide warning system. J Ambient Intell Human Comput 14, 11165–11172 (2023). https://doi.org/10.1007/s12652-022-04396-6

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  • DOI: https://doi.org/10.1007/s12652-022-04396-6

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