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Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method

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Abstract

Global climate change and human activities aggravate the frequency of flood disasters. Flood risk includes natural flood risk and risk of economic and social disasters, which is displayed intuitively by flood risk zonation maps. In this paper, we take the disaster-causing factors, the disaster environment, the disaster-bearing body, and the disaster prevention and mitigation capability into consideration comprehensively. Eleven influencing indexes including annual maximum 3-day rainfall and rainfall in flood season are selected, and the virtual sown area of crops is innovated. Taking the Huaihe River Basin (HRB) as the research area, the flood risk prediction of the basin is explored by using the long short-term memory (LSTM). The results show that LSTM can be successfully applied to flood risk prediction. The short-term prediction results of the model are good, and the area where the risk is seriously underestimated (the high and very high risk are identified as the very low risk) accounts for only 0.98% of the total basin on average. The prediction results can be used as a reference for watershed management organizations, so as to guide future flood disaster prevention.

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Acknowledgements

This work was supported by the National Key R&D Program of China(Grant No. 2017YFC0405606), the National Natural Science Foundation of China(Grant No. 51579068), the Foundamental Research Funds for the Central Universities(Grant No. 2018B10514, 2018B18214, 2018B606X14, and 2019B72414), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant Nos. SJKY19_0468 and KYCX18_0578), the National Natural Science Foundation of Jiangsu Province, China(Grant No. BK20180509), the China Postdoctoral Science Foundation (Grant No. 2017M621612, 2018T110525, 2019M661715), and the National Natural Science Foundation of China(Grant No. 51579068, 51609062, 51909062).

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Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, No. 1, Xikang Road, Nanjing, 210098, China

    Minzhi Yang, Ping-an Zhong, Jieyu Li & Weifeng Liu

  2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, No. 1, Xikang Road, Nanjing, 210098, China

    Ping-an Zhong

  3. College of Agriculture and Water Conservancy Engineering, Suihua University, Yellowriver Road, Suihua, 152061, China

    Yinghui Li

  4. Zhejiang Institute of Hydraulics & Estuary, No. 50, Fengqi East Road, Hangzhou, 310020, China

    Kun Yan & Yihui Gao

  5. Chengdu Water Authority, No. 68, Shujin Road, Chengdu, 610042, China

    Yinyang Yuan

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  1. Minzhi Yang
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Yang, M., Zhong, Pa., Li, J. et al. Research on intelligent prediction and zonation of basin-scale flood risk based on LSTM method. Environ Monit Assess 192, 387 (2020). https://doi.org/10.1007/s10661-020-08351-w

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