Skip to main content

A New Economic Loss Assessment System for Urban Severe Rainfall and Flooding Disasters Based on Big Data Fusion

  • Chapter
  • First Online:
Economic Impacts and Emergency Management of Disasters in China

Abstract

Background and Purpose: Increasingly frequent meteorological disasters have brought severe challenges that should be urgently handled in the sustainable development. However, meteorological data, loss data, social economic data and so forth relating to meteorological disasters rarely be effectively fused, failing to generate, rapidly and efficiently, economic losses and thus hindering the emergency management of disasters. Methods: A new economic losses evaluation information system has been developed for monitoring severe rainfall and flooding disasters in cities. The data mining method, econometric regression model and input–output model are implemented in the system, on the basis of multi-source data including hourly rainfall, geographical conditions, historical and real-time disaster information, socioeconomic data, and defense countermeasure. Results: Combined with the weather forecast information, this system can has the capability for reporting the real-time direct and indirect economic losses incurred by urban heavy rainfall and flooding disasters, automatically generating defense countermeasure reports for typical rainstorm and flooding points, and providing the spatial distribution of disasters. Conclusions: Finally, the system is conducive to improving the ability to manage disaster emergencies and eventually reducing the economic losses from the disaster.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Amadio, M., Mysiak, J., Carrera, L., & Koks, E. (2016). Improving flood damage assessment models in Italy. Natural Hazards,82(3), 2075–2088.

    Article  Google Scholar 

  • Anbarasan, M., Muthu, B., Sivaparthipan, C. B., Sundarasekar, R., Kadry, S., Krishnamoorthy, S., et al. (2020). Detection of flood disaster system based on loT, big data and convolutional deep neural network. Computer Communications,150, 150–157.

    Article  Google Scholar 

  • Antimiani, A., Costantini, V., & Paglialunga, E. (2015). The sensitivity of climate-economy CGE models to energy-related elasticity parameters: Implications for climate policy design. Economic Modelling,51, 38–52.

    Article  Google Scholar 

  • Avelino, F. T. A., & Dall’erba, S. (2018). Comparing the economic impact of natural disasters generated by different input-output models: An application to the 2007 chehalis river flood (WA). Risk Analysis,13006, 85–104.

    Google Scholar 

  • Baghersad, M., & Zobel, C. W. (2015). Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors. International Journal of Production Economics,168(10), 71–80.

    Article  Google Scholar 

  • Barker, K., & Santos, J. R. (2010). Measuring the efficacy of inventory with a dynamic input–output model. International Journal of Production Economics, 126(1), 130–143.

    Google Scholar 

  • Boström, H., Andler, S., Brohede, M., Johansson, R., Karlsson, A., Laere, J. V., Niklasson, L., Klingegard, M., Persson, A., & Ziemke, T. (2007). On the definition of information fusion as a field of research. Informatics Research Centre, University of Skovde, Tech. Rep. HS-IKI-TR-07–006. Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1256.

  • Carrera, L., Standardi, G., & Bosello, F. (2015). Assessing direct and indirect economic impacts of a flood event through the integration of spatial and computable general equilibrium modelling. Environmental Modelling & Software,63(11), 109–122.

    Article  Google Scholar 

  • Crowther, K. G., & Haimes, Y. Y. (2007). Systemic valuation of strategic preparedness through application of the inoperability input-output model with lessons learned from hurricane Katrina. Risk Analysis,27(5), 1345–1364.

    Article  Google Scholar 

  • Cui, Q., Xie, W., & Liu, Y. (2018). Effects of sea level rise on economic development and regional disparity in China. Journal of Cleaner Production,176(1), 1245–1253.

    Article  Google Scholar 

  • D’Ayala, D., Wang, K., Yan, Y., Smith, H., Massam, A., Filipova, V., & Pereira, J. J. (2020). Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of kuala lumpur, malaysia. Natural Hazards and Earth System Sciences,20(8), 2221–2241.

    Article  Google Scholar 

  • Dabbeek, J., Silva, V., Galasso, C., & Smith, A. (2020). Probabilistic earthquake and flood loss assessment in the middle east. International Journal of Disaster Risk Reduction,49, 101662.

    Article  Google Scholar 

  • Das, S., & Lee, R. (1988). A nontraditional methodology for flood stage-damage calculations. Jawra Journal of the American Water Resources Association,24(6), 1263–1272.

    Article  Google Scholar 

  • De, Jonge, T., Kok, M., & Hogeweg, M. (1996). Modelling floods and damage assessment using GIS. Iahs publication, 299–306.

    Google Scholar 

  • Enke, D. (2007). Case study: Applying a regional CGE model for estimation of indirect economic losses due to damaged highway bridges. Engineering Economist.,52(4), 367–401.

    Article  Google Scholar 

  • Galbusera, L., & Giannopoulos, G. (2018). On input-output economic models in disaster impact assessment. International Journal of Disaster Risk Reduction,3, 0186–0198.

    Article  Google Scholar 

  • Gao, Z. Q., Richard, G., & Tao, M. (2020). Direct and indirect economic losses using typhoon-flood disaster analysis: An application to Guangdong province, China. Sustainability,12(21), 8980.

    Article  Google Scholar 

  • Gertz, A. B., Davies, J. B., & Black, S. L. (2019). A CGE framework for modeling the economics of flooding and recovery in a major urban area. Risk Analysis,39(6), 1314–1341.

    Article  Google Scholar 

  • Haimes, Y. Y., & Jiang, P. (2001). Leontief-based model of risk in complex interconnected infrastructures. Journal of Infrastructure Systems,7(1), 1–12.

    Article  Google Scholar 

  • Hallegatte, S. (2008). An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Analysis,28(3), 779–799.

    Article  Google Scholar 

  • Hallegatte, S. (2015). The indirect cost of natural disasters and an economic definition of macroeconomic resilience. World Bank Policy Research Working Paper, 1–37.

    Google Scholar 

  • Haq, M., Akhtar, M., & Muhammad, S. (2012). Techniques of remote sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science,15(2), 135–141.

    Article  Google Scholar 

  • Helbing, D. (2013). Globally networked risks and how to respond. Nature,497, 51–59.

    Article  Google Scholar 

  • Hisamatsu, R., Tabeta, S., Kim, S., & Mizuno, K. (2020). Storm surge risk assessment for the insurance system: A case study in Tokyo Bay, Japan. Ocean and Coastal Management,189, 105147.

    Article  Google Scholar 

  • Hou, W., Chen, Z. Q., Zuo, D. D., & Feng, G. L. (2019). Drought loss assessment model for southwest China based on a hyperbolic tangent function. International Journal of Disaster Risk Reduction,33, 477–484.

    Article  Google Scholar 

  • Huang, C. F. (2012). Risk analysis and management of natural disaster. Beijing: Science Press. (in Chinese).

    Google Scholar 

  • Huang, C. F., & Huang, Y. D. (2018). An information diffusion technique to assess integrated hazard risks. Environmental Research,161, 104–113.

    Article  Google Scholar 

  • IPCC. (2014). IPCC[R]. https://www.ipcc.ch.

  • Jeon, H., Eem, S. H., & Park, J. (2018). Flood damage assessment in building scale caused by the coastal inundation height at Haeundae beach, Busan. Journal of Coastal Research, 1561–1565.

    Google Scholar 

  • Jin, X., Rashid, S., & Kedong, Y. (2020). Direct and indirect loss evaluation of storm surge disaster based on static and dynamic input-output models. Sustainability,12(18), 7347.

    Article  Google Scholar 

  • Kappes, M. S., Keiler, M., Von, E. K., & Glade, T. (2012). Challenges of analyzing multi-hazard risk: A review. Natural Hazards,64, 1925–1958.

    Article  Google Scholar 

  • Kazama, S., Sato, A., & Kawagoe, S. (2010). Evaluating the cost of flood damage based on changes in extreme rainfall in Japan. Adaptation and Mitigation Strategies for Climate Change. Springer Japan, 3–17.

    Google Scholar 

  • Kefi, M., Mishra, B. K., Kumar, P., Masago, Y., & Fukushi, K. (2018). Assessment of tangible direct flood damage using a spatial analysis approach under the effects of climate change: Case study in an urban watershed in Hanoi, Vietnam. ISPRS International Journal of Geo-Information,7(1), 29.

    Article  Google Scholar 

  • Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion,14, 28–44.

    Article  Google Scholar 

  • Kilimani, N., van, Heerden, J., Bohlmann, H., & Roos, L. (2018). Economy-wide impact of drought induced productivity losses. Disaster Prevention and Management, 27(5), 636–648.

    Google Scholar 

  • Koks, E. E., Carrera, L., & Jonkeren, O. (2015). Regional disaster impact analysis: Comparing input-output and computable general equilibrium models. Natural Hazards & Earth System Sciences Discussions, 3(11), 7053–7088.

    Google Scholar 

  • Koks, E. E., Carrera, L., & Jonkeren, O. (2016). Regional disaster impact analysis: Comparing input–output and computable general equilibrium models. Natural Hazards and Earth System Sciences,16(8), 1911–1924.

    Article  Google Scholar 

  • Komolafe, A. A., Herath, S., & Avtar, R. (2019). Establishment of detailed loss functions for the urban flood risk assessment in Chao Phraya River basin, Thailand. Geomatics Natural Hazards and Risk,10(1), 633–650.

    Article  Google Scholar 

  • Liu, H. B. (2015). A study on economic loss assessment of heavy rain and waterlogging disaster in Shenzhen city from the perspective of highway traffic industry. Nanjing University of Information Science & Technology.

    Google Scholar 

  • Liu, X. Q., Yuan, S., Chen, Z. H., Song, L. N., Ma, Y. X., Wang, C. L., & Wu, J. D. (2019). Assessing the indirect economic losses of sea ice disasters: An adaptive regional input-output modeling approach. International Journal of Offshore and Polar Engineering,28(4), 415–420.

    Article  Google Scholar 

  • Li, W. J., Wen, J. H., Xu, B., Li, X. D., & Du, S. Q. (2019). Integrated assessment of economic losses in manufacturing industry in shanghai metropolitan area under an extreme storm flood scenario. Sustainability,11(1), 126.

    Article  Google Scholar 

  • Llinas, J. (2002). Information fusion for natural and man-made disasters. Information Fusion, Proceedings of the Fifth International Conference on Information Fusion. IEEE, 1,570–576.

    Google Scholar 

  • Mackenzie, C. A., Santos, J. R., & Barker, K. (2012). Measuring changes in international production from a disruption: Case study of the Japanese earthquake and tsunami. International Journal of Production Economics,138(2), 293–302.

    Article  Google Scholar 

  • Mendoza-Tinoco, D., Guan, D., & Zeng, Z. (2017). Flood footprint of the 2007 floods in the UK: The case of the Yorkshire and The Humber region. Journal of Cleaner Production,168(1), 655–667.

    Article  Google Scholar 

  • Mendoza-Tinoco, D., Guan, D. B., Zeng, Z., Xia, Y., & Serrano, A. (2017). Flood footprint of the 2007 floods in the UK: The case of the Yorkshire and The Humber region. Journal of Cleaner Production,168, 655–667.

    Article  Google Scholar 

  • Middelmann-Fernandes, M. H. (2010). Flood damage estimation beyond stage–damage functions: An Australian example. Journal of Flood Risk Management.,3(1), 88–96.

    Article  Google Scholar 

  • Mohammadia, S. A., & Nazarihaa, M. (2014). Flood damage estimate (quantity), using HEC-FDA model. Case study: The Neka river. Procedia Engineering, 70, 1173–1182.

    Google Scholar 

  • MRl, H. M. H. (2003). Multi-hazard loss estimation methodology: Earthquake model. Department of Homeland Security, FEMA, Washington, DC.

    Google Scholar 

  • Narayan, P. K. (2003). Macroeconomic impact of natural disasters on a small island economy: Evidence from A CGE Model. Applied Economics Letters,10(11), 721–723.

    Article  Google Scholar 

  • Notaro, V., De, Marchis, M., & Fontanazza, C. M. (2014). The effect of damage functions on urban flood damage appraisal. Procedia Engineering, 70, 1251–1260.

    Google Scholar 

  • Okuyama, Y. (2007). Economic modeling for disaster impact analysis: Past, present, and future. Economic Systems Research,19(2), 115–124.

    Article  Google Scholar 

  • Okuyama, Y. (2010). Globalization and localization of disaster impacts: an empirical examination. General Information,11(2), 56–66.

    Google Scholar 

  • Oliver, E., & Santoro, M. (2000). Estimation of urban structural flood damages: The case study of Palermo. Urban Water.,2(3), 223–234.

    Article  Google Scholar 

  • Pang, S. L., Li, S. H., & Hu, X. F. (2020). Typhoon carrier disaster loss index models and application based on principal component analysis. Journal of Coastal Research, 68–72.

    Google Scholar 

  • Pinos, J., Orellana, D., & Timbe, L. (2020). Assessment of microscale economic flood losses in urban and agricultural areas: Case study of the santa Bárbara river, Ecuador. Natural Hazards,103(2), 2323–2337.

    Article  Google Scholar 

  • Ploeger, S. K., Atkinson, G. M., & Samson, C. (2010). Applying the HAZUS-MH software tool to assess seismic risk in downtown Ottawa, Canada. Natural Hazards,53, 1–20.

    Article  Google Scholar 

  • Puttinaovarat, S., & Horkaew, P. (2020). Flood forecasting system based on integrated big and crowdsource data by using machine learning techniques. IEEE Access,8, 5885–5905.

    Article  Google Scholar 

  • Rahman, M. S., Di, L. P., Yu, E., Lin, L., & Yu, Z. Q. (2020). Remote sensing based rapid assessment of flood crop damage using novel disaster vegetation damage index (dvdi). International Journal of Disaster Risk Science.

    Google Scholar 

  • Ring, I., HansjĂĽrgens, B., & Elmqvist, T. (2010). Challenges in framing the economics of ecosystems and biodiversity: The TEEB initiative. Current Opinion in Environmental Sustainability,2(1), 15–26.

    Article  Google Scholar 

  • Rose, A. (2004).Economic principles, issues, and research priorities in Hazard Loss estimation. In: Y. Okuyama & S. E Chang (Eds.), Modeling Spatial and Economic Impacts of Disasters (pp. 13–36). New York: Springer.

    Google Scholar 

  • Rose, A., & Liao, S. Y. (2005). Modeling regional economic resilience to disasters: A computable general equilibrium analysis of water service disruptions. Journal of Regional Science,45(1), 75–112.

    Article  Google Scholar 

  • Rose, A., & Lim, D. (2002). Business interruption losses from natural hazards: Conceptual and methodological issues in the case of the Northridge earthquake. Global Environmental Change Part B Environmental Hazards,4(1), 1–14.

    Article  Google Scholar 

  • Scawthorn, C., Blais, N., Seligson, H., Tate, E., & Jones, C. (2006). Hazus-mh flood loss estimation methodology. I: overview and flood hazard characterization. Natural Hazards Review, 7(2), 72–81.

    Google Scholar 

  • Sieg, T., Schinko, T., Vogel, K., Mechler, R., Merz, B., & Kreibich, H. (2019). Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification. PLoS ONE,14(4), e0212932.

    Article  Google Scholar 

  • Sun, S., Shi, H., & Wu, Y. (2014). A survey of multi-source domain adaptation. Information Fusion,24, 84–92.

    Article  Google Scholar 

  • Tang, J. C. S., Vongvisessomjai, S., & Sahasakmontri, K. (1992). Estimation of flood damage cost for Bangkok. Water Resources Management,6(1), 47–56.

    Article  Google Scholar 

  • Tanoue, M., Nabe, S., Fujimori, S., Hirabayashi, Y. Estimation of direct and indirect economic losses caused by a flood with long-lasting inundation: Application to the 2011 Thailand flood. Water Resources Research, 56(5), e2019WR026092.

    Google Scholar 

  • Tan, L., Wu, X. H., & Xu, Z. S. (2019). Comprehensive economic loss assessment of disaster based on CGE Model and IO Model—A case study on Beijing “7.21 Rainstorm”. International Journal of Disaster Risk Reduction, 39, 101246.

    Google Scholar 

  • Tatano, H., & Tsuchiya, S. (2008). A framework for economic loss estimation due to seismic transportation network disruption: A spatial computable general equilibrium approach. Natural Hazards,44(2), 253–265.

    Article  Google Scholar 

  • Thirawat, N., Udompol, S., & Ponjan, P. (2017). Disaster risk reduction and international catastrophe risk insurance facility. Mitigation and Adaptation Strategies for Global Change,22(7), 1021–1039.

    Article  Google Scholar 

  • Tinoco, D. M. (2018). Alba VerĂłnica MĂ©ndez Delgado. EvaluaciĂłn de los costos econĂłmicos totales de los desastres naturales: inundaciĂłn en la ciudad de sheffield, 2007. Estudios Demográficos y Urbanos, 33(3), 699.

    Google Scholar 

  • Vickery, P. J., Skerlj, P. F., Lin, J., Twisdale, J., Young, M. A., & Lavelle, F. M. (2006). Hazus-MH hurricane model methodology. II: Damage and loss estimation. Natural Hazards Review, 7,94–103.

    Google Scholar 

  • Wang, C. H., Chen, W. N., Zhang, J., Song, S., & Lu, R. Q. (2014). Challenging scientific problems for technologies and applications of big data. Bulletin of National Natural Science Foundation of China,2, 92–98.

    Google Scholar 

  • Wang, G. Z., Li, X., Wu, X. H., & Yu, J. (2015). The rainstorm comprehensive economic loss assessment based on CGE model: Using a July heavy rainstorm in Beijing as an example. Natural Hazards,76(2), 839–854.

    Article  Google Scholar 

  • Wang, D., Huang, C., & Mai, B. (2016). To facilitate the advance of risk analysis and crisis response in China. Environmental Research,148, 547–549.

    Article  Google Scholar 

  • Wang, G. Z., Wu, L. Y., & Chen, J. B. (2016). Intensity and economic loss assessment of the snow, low-temperature and frost disasters: A case study of Beijing City. Natural Hazards,84(1), 293–307.

    Article  Google Scholar 

  • Wang, G. Z., Chen, R. R., & Chen, J. B. (2017). Direct and indirect economic loss assessment of typhoon disasters based on EC and IO joint model. Natural Hazards,87(3), 1751–1764.

    Article  Google Scholar 

  • Weitzman, M. L. (2009). On modeling and interpreting the economics of catastrophic climate change. Review of Economics & Statistics,91(1), 1–19.

    Article  Google Scholar 

  • Wu, S., & Crestani, F. (2015). A geometric framework for data fusion in information retrieval. Information Systems,50, 20–35.

    Article  Google Scholar 

  • Wu, X. H., Zhou, L., & Gao, G. (2016). Urban flood depth-economic loss curves and their amendment based on resilience: Evidence from Lizhong Town in Lixia River and Houbai Town in Jurong River of China. Natural Hazards,82, 1981–2000.

    Article  Google Scholar 

  • Wu, X. H., Xue, P. P., Guo, J., Ji, Z. H., Wei, G., & Ning, X. Q. (2017). On the amount of counterpart assistance to be provided after natural disasters: From the perspective of indirect economic loss assessment. Global Environmental Change Part B Environmental Hazards,16(1), 50–70.

    Google Scholar 

  • Wu, X.H., Cao, Y. l., Xiao, Y., & Guo, J. (2018). Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics. Annals of Operations Research, 1–32.

    Google Scholar 

  • Wu, X. H., Xu, Z., Liu, H., Guo, J., & Zhou, L. (2018). What are the impacts of tropical cyclones on employment? An analysis based on meta-regression. Weather Climate & Society,11(2), 259–275.

    Article  Google Scholar 

  • Wu, X. H., Guo, J., & Gonzalez, E. D. R. S. (2019). Determining the amount of international aid that countries should donate after a disaster to alleviate sustainable implications: A new framework for analysis. Journal of Cleaner Production,241(12), 118285.

    Article  Google Scholar 

  • Wu, Z. N., Shen, Y. X., Wang, H. L., & Wu, M. M. (2020). Quantitative assessment of urban flood disaster vulnerability based on text data: Case study in Zhengzhou. Water Science and Technology-Water Supply,20(2), 408–415.

    Article  Google Scholar 

  • Xia, Y., Li, Y., Guan, D. B., David, T., Xia, J. J., Yan, Z. W., et al. (2018). Assessment of the economic impacts of heat waves: A case study of Nanjing, China. Journal of Cleaner Production,171, 811–819.

    Article  Google Scholar 

  • Xie, W., Rose, A., & Li, S. (2018). Dynamic economic resilience and economic recovery from disasters: a quantitative assessment. Risk Analysis,38(6), 1306–1318.

    Article  Google Scholar 

  • Yang, T. M., Chen, J. H., & Chen, X. Y. (2005). Study on the knowledge base system of agricultural meteorological disasters and Defense Countermeasures in Anhui Province. Journal of Anhui Agricultural Sciences,33, 1682–1683.

    Google Scholar 

  • Ye, X. L., Wen, J. H., Zhu, Z. F., & Sun, R. H. (2020). Natural disaster risk assessment in tourist areas based on multi scenario analysis. Earth Science Informatics.

    Google Scholar 

  • Zhang, W. H. (2020). Geological disaster monitoring and early warning system based on big data analysis. Arabian Journal of Geo Sciences,13(18), 946.

    Article  Google Scholar 

  • Zhang, Z. T., Li, N., Wei, X., Yu, L., Feng, J. L., Xi, C., & Liu, L. (2017). Assessment of the ripple effects and spatial heterogeneity of total losses in the capital of china after a great catastrophic shock. Natural Hazards and Earth System Ences,17(3), 1–21.

    Google Scholar 

  • Zhang, S. H., Yang, K., & Cao, Y. B. (2019). GIS-based rapid disaster loss assessment for earthquakes. IEEE Access,7, 6129–6139.

    Article  Google Scholar 

  • Zhang, Z. T., Li, N., Xu, H., Feng, J. L., Chen, X., Gao, C., & Zhang, P. (2019). Allocating assistance after a catastrophe based on the dynamic assessment of indirect economic losses. Natural Hazards,99(1), 17–37.

    Article  Google Scholar 

  • Zheng, G. G. (2014). Annual 2000–3000 billion yuan of economic losses caused by meteorological disasters in China. Retrieved from https://www.ce.cn/.

  • Zheng, Y., & Sun, H. (2020). An integrated approach for the simulation modeling and risk assessment of coastal flooding. Water,12(8), 2076.

    Article  Google Scholar 

  • Zhou, S. T., Zhai, G. F., Shi, Y. J., & Lu, Y. W. (2020). Urban seismic risk assessment by integrating direct economic loss and loss of statistical life: An empirical study in Xiamen, China. International Journal of Environmental Research and Public Health,17(21), 8154.

    Article  Google Scholar 

  • Zhu, Z. H. (2010). Study of data warehouse and data mining application on geohazard in three gorges reservior area. Ph. D. dissertation (in Chinese), Institute of Engineering, China University of Geosciences, 121.

    Google Scholar 

Download references

Acknowledgements

Guo Wei, Tingting Feng also made great contributions to this manuscript. We express our heartfelt thanks to them. This research was supported by: National Social and Scientific Fund Program of China (18ZDA052; 17BGL142;16ZDA047); The Natural Science Foundation of China (91546117, 71373131).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xianhua Wu .

Appendix

Appendix

See Table 9.2.

Table 9.2 Description of symbols in this paper
Table 9.3 Description of abbreviations in this paper

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wu, X., Guo, J. (2021). A New Economic Loss Assessment System for Urban Severe Rainfall and Flooding Disasters Based on Big Data Fusion. In: Economic Impacts and Emergency Management of Disasters in China. Springer, Singapore. https://doi.org/10.1007/978-981-16-1319-7_9

Download citation

Publish with us

Policies and ethics