Skip to main content
SpringerLink
Log in

Forecast of flood disaster emergency material demand based on IACO-BP algorithm

  • Special Issue on Multi-modal Information Learning and Analytics on Big Data
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The frequent occurrence of various sudden natural disasters in the world has caused heavy losses to human beings. It is very important to forecast the demand of emergency materials in order to protect people's safety and property. The purpose of this study is to use IACO-BP algorithm to forecast the demand of emergency supplies in the case of flood disaster. In this study, the flood disaster situation in recent years published by the Ministry of Water Resources of China is selected as the experimental data set. The data are sorted and analyzed by a variety of theoretical comprehensive operation, qualitative and quantitative research method, analytic hierarchy process and system dynamics method. The improved ant colony optimization algorithm is used to model the emergency material demand, and the population, flood level, flood level of the disaster area are analyzed. As the network input, the material situation outputs the material demand, so as to forecast. The results show that the iteration times of IACO-BP algorithm are 11 and the running time is 3S. The fluctuation of IACO-BP algorithm is the least and the most stable among the three algorithms. The material satisfaction degree predicted by IACO-BP algorithm is improved by 15% from the original 80.9%. It is concluded that this algorithm is very accurate and efficient in the prediction of emergency material demand, which can better assist the disaster situation. This study contributes to the prediction of emergency material demand for emergency disaster.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Data source: emergency disaster database (me-dat)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Chen AY, Lu TY, Ma HM et al (2016) Demand Forecast using data analytics for the preallocation of ambulances. IEEE J Biomed Health Inform 20(4):1178–1187

    Article  Google Scholar 

  2. Kwon HY, Kang YO (2016) Risk analysis and visualization for detecting signs of flood disaster in Twitter. Spatial Inf Res 24(2):127–139

    Article  Google Scholar 

  3. Khan MG et al (2017) Communities’ local knowledge of flood disaster management in Pakistan. Adv Sci Lett 23(7):6087–6090

    Article  Google Scholar 

  4. Chung J (2016) Conflicts and natural disaster management: a comparative study of flood control in the Republic of Korea and the United States. Disasters 40(3):554–572

    Article  Google Scholar 

  5. Su M, Zheng Y, Hao Y et al (2017) The influence of landscape pattern on the risk of urban water-logging and flood disaster. Ecol Indic 92(9):133–140

    Google Scholar 

  6. Cipolla E, Maniscalco U, Rizzo R et al (2017) Analysis and visualization of meteorological emergencies. J Ambient Intell Hum Comput 8(1):57–68

    Article  Google Scholar 

  7. Zuo J, Lyv M, Zhang ZH et al (2019) Research on the Process Supervision and Forecasting Model of Railway Emergency Based on GERTS. Int J Pattern Recognit Artif Intell 33(1):1959003.1-1959003.17

    Article  Google Scholar 

  8. Jilani T, Housley G, Figueredo G et al (2019) Short and Long term predictions of Hospital emergency department attendances. Int J Med Inf 129(9):167–174

    Article  Google Scholar 

  9. Suh DY, Ryerson MS (2019) Forecast to grow: aviation demand forecasting in an era of demand uncertainty and optimism bias. Transp Res 128(8):400–416

    Google Scholar 

  10. Aneiros G, Vilar J, Rana P (2016) Short-term forecast of daily curves of electricity demand and price. Int J Electr Power Energy Syst 80(9):96–108

    Article  Google Scholar 

  11. Herrera CA (2017) Proposing a physiological model for Emergency Department. Operating principles, classification of overcrowding and guidelines for redesign. Anales del sistema sanitario de Navarra 40(1):11–24

    Article  Google Scholar 

  12. Aerts JCJH, Botzen WJ, Clarke KC et al (2018) Integrating human behaviour dynamics into flood disaster risk assessment. Nat Climate Change 8(3):193–199

    Article  Google Scholar 

  13. Bower B (2016) Flood tale rooted in real disaster: deluge might have given china’s first dynasty wet start. Sci News 190(5):6–6

    Google Scholar 

  14. Wang Y, Yuan X (2016) Comprehensive evaluation of urdan flood control and disaster reduction ability based on principal component analysis. J Nat Disasters 25(6):1–8

    Google Scholar 

  15. Khantong S, Ahmad MN (2020) An ontology for sharing and managing information in disaster response: in flood response usage scenarios. J Data Semant 9(1):39–52

    Article  Google Scholar 

  16. Ni Y, Uebele M (2019) Size and structure of disaster relief when state capacity is limited: China’s 1823 flood. Aust Econ Hist Rev Asia-Pac J Econ Bus Soc Hist 59(1):24–54

    Google Scholar 

  17. Aerts JCJH, Botzen WJ, Clarke KC et al (2018) Integrating human behaviour dynamics into flood disaster risk assessment. Nat Clim Change 8(3):193–199

    Article  Google Scholar 

  18. Yu-Yan W, Yan-Jun W, Tong J (2016) Spatial-temporal characteristics of exposure and vulnerability to flood disaster in Jiangsu Province. J Yangtze River Sci Res Inst 33(4):27–32

    Google Scholar 

  19. Wan H, Song H, Zhu C et al (2018) Spatio-temporal evolution of drought and flood disaster chains in Baoji area from 1368 to 1911. J Geogr Sci 28(3):337–350

    Article  Google Scholar 

  20. Li K, Gao W, Zhao Y et al (2017) Research on flood disaster in north Henan area during the Medieval Warm Period. J Nat Disasters 26(2):81–89

    Google Scholar 

  21. He YH, Jiang RC, Gu SX (2017) Applied prospect of modern information technology in relation to mountain flood disaster monitoring and early warning system. J Inf Optim Sci 38(7):1151–1167

    Google Scholar 

  22. Seung HL et al (2017) Development of compression and transmission technology of GIS-based high resolution image data in flood disaster situation. J Korea Multimedia Soc 20(7):1038–1045

    Google Scholar 

  23. Nguyen NY, Ichikawa Y, Ishidaira H (2017) Establishing flood damage functions for agricultural crops using estimated inundation depth and flood disaster statistics in data-scarce regions. Hydrol Res Lett 11(1):12–18

    Article  Google Scholar 

  24. Chinnarasri C, Phothiwijit K (2016) Appropriate engineering measures with participation of community for flood disaster reduction: case of the Tha Chin Basin. Thailand Arab J Sci Eng 41(12):4879–4892

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Emergency Science, Xihua University, Chengdu, 610039, Sichuan, China

    Fujiang Chen

  2. China Railway Academy Co., Ltd., Chengdu, 610032, Sichuan, China

    Junying Chen & Jingang Liu

Authors
  1. Fujiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fujiang Chen.

Ethics declarations

Conflict of interest

There are no potential competing interests in our paper. And all authors have seen the manuscript and approved submission of your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Chen, J. & Liu, J. Forecast of flood disaster emergency material demand based on IACO-BP algorithm. Neural Comput & Applic 34, 3537–3549 (2022). https://doi.org/10.1007/s00521-021-05883-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05883-1

Keywords

Search

Navigation