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Application of a novel artificial neural network model in flood forecasting

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Abstract

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. The topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Thanks are given to the anonymous reviewers and editors.

Funding

This study has been financially supported by the National Natural Science Foundation of China, China (41830863; 51879162), the National Key Research and Development Programs of China, China (2021YFC3201100, 2017YFA0605002; 2017YFC0404401; 2017YFC0404602, 2016YFA0601501), and the Belt and Road Fund on Water and Sustainability of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, China (2019nkzd02; (2020nkzd01).

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Authors and Affiliations

  1. Hydrology and Water Resources Monitoring and Forecasting Center, Ministry of Water Resources, Beijing, 100053, China

    Guangsheng Wang & Zhijie Yin

  2. Department of Hydrology, Ministry of Water Resources, Beijing, 100053, China

    Jianqing Yang

  3. Hydrology Bureau of Anhui Province, Hefei, 230000, Anhui, China

    Yuzhong Hu & Jingbing Li

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  1. Guangsheng Wang
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  2. Jianqing Yang
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  3. Yuzhong Hu
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  4. Jingbing Li
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  5. Zhijie Yin
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Correspondence to Guangsheng Wang.

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Wang, G., Yang, J., Hu, Y. et al. Application of a novel artificial neural network model in flood forecasting. Environ Monit Assess 194, 125 (2022). https://doi.org/10.1007/s10661-022-09752-9

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