Research on the Application of Machine Learning Technology in Hydrological Flood Prediction
DOI:
https://doi.org/10.71222/se6cyv71Keywords:
urban floods, flood forecasting, machine learning, hydrologyAbstract
Urban flooding disasters frequently occur in our country, severely affecting the national development process, anticipating the probability and severity of floods can effectively reduce the negative impacts caused by floods, the rapid progress of hydrology has accelerated the development of flood prediction research. Currently, a lot of machine learning methods are widely applied in the field of flood forecasting based on hydrology, which holds great significance for social development. First, the hydrological models currently used for flood forecasting are introduced. Then, the application of machine learning models in hydrology is elaborated. Finally, the problems and challenges faced by machine learning in flood prediction are analyzed and summarized, and prospects for future flood prediction technologies are discussed.
References
1. C. Deng and W. Wang, "A two-stage partitioning monthly model and assessment of its performance on runoff modeling," J. Hydrol., vol. 592, 2021, doi: 10.1016/j.jhydrol.2020.125829.
2. P. A. Mendoza, J. McPhee, and X. Vargas, "Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment," Water Resources Research, vol. 48, no. 9, 2012, doi: 10.1029/2011WR011089.
3. E. F. Wood, J. K. Roundy, T. J. Troy, et al., "Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water," Water Resour. Res., vol. 47, no. 5, 2011, doi: 10.1029/2010WR010090.
4. C. Paniconi and M. Putti, "Physically based modeling in catchment hydrology at 50: Survey and outlook," Water Resour. Res., vol. 51, pp. 7090-7129, 2015, doi: 10.1002/2015WR017780.
5. M. Valipour, M. E. Banihabib, and S. M. R. Behbahani, "Parameters estimate of autoregressive moving average and auto-regressive integrated moving average models and compare their ability for inflow forecasting," J. Math. Stat., vol. 8, no. 3, pp. 330-338, 2012, doi: 10.3844/jmssp.2012.330.338.
6. K. Haddad and A. Rahman, "Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework-Quantile Regression vs. Parameter Regression Technique," J. Hydrol., vol. 430-431, pp. 142-161, 2012, doi: 10.1016/j.jhydrol.2012.02.012.
7. C. N. Kroll and R. M. Vogel, "Probability distribution of low streamflow series in the United States," J. Hydrol. Eng., vol. 7, no. 2, pp. 137-146, 2002, doi: 10.1061/(ASCE)1084-0699(2002)7:2(137).
8. M. Pan, H. Zhou, J. Cao, Y. Liu, J. Hao, S. Li, & C.-H. Chen., "Water Level Prediction Model Based on GRU and CNN," IEEE Access, vol. 8, pp. 60090-60100, 2020, doi: 10.1109/ACCESS.2020.2982433.
9. Y. B. Dibike, S. Velickov, D. Solomatine, et al., "Model induction with support vector machines: Introduction and applications," J. Comput. Civil Eng., 2001, doi: 10.1061/(ASCE)0887-3801(2001)15:3(208).
10. P.-S. Yu, T.-C. Yang, S.-Y. Chen, C.-M. Kuo, and H.-W. Tseng, "Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting," J. Hydrol., vol. 552, pp. 92-104, 2017, doi: 10.1016/j.jhydrol.2017.06.020.
11. J. F. Adamowski, "Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis," J. Hydrol., vol. 353, no. 3, pp. 247-266, 2008, doi: 10.1016/j.jhydrol.2008.02.013.
12. J. Shiri and O. Kisi, "Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model," J. Hydrol., vol. 394, no. 3-4, pp. 486-493, 2010, doi: 10.1016/j.jhydrol.2010.10.008.
13. L. Liu, X. Liu, P. Bai, et al., "Comparison of flood simulation capabilities of a hydrologic model and a machine learning model," Int. J. Climatol., 2023, doi: 10.1002/joc.7738.
14. F. J. Chang, P. A. Chen, Y. R. Lu, et al., "Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control," J. Hydrol., vol. 517, pp. 836-846, 2014, doi: 10.1016/j.jhydrol.2014.06.013.
15. N. Q. Hung, M. S. Babel, S. Weesakul, N. K. J. H. Tripathi, and E. S. Sciences, "An artificial neural network model for rainfall forecasting in Bangkok, Thailand," 2009, vol. 13, doi: 10.5194/hess-13-1413-2009.
16. Y.-M. C. Yen-Ming, L.-C. C. Li-Chiu, T. M.-J. T., et al., "Dynamic neural networks for real-time water level predictions of sew-erage systems—covering gauged and ungauged sites," Hydrol. Earth Syst. Sci., vol. 14, no. 7, pp. 2317-2345, 2010, doi: 10.5194/hess-14-1309-2010.
17. D. T. Bui, N. D. Hoang, and M. Martinez-Alvarez, et al., "A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area," Sci. Total Environ., vol. 701, p. 134413, 2020, doi: 10.1016/j.scitotenv.2019.134413.
18. J. P. Leito, N. E. Simes, Z. Guo, et al., "Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks," J. Flood Risk Manage., vol. 14, no. 1, pp. n/a-n/a, 2021, doi: 10.1111/jfr3.12684.
19. A. R. Lima, A. J. Cannon, and W. W. Hsieh, "Forecasting daily streamflow using online sequential extreme learning machines," J. Hydrol., vol. 537, pp. 431-443, 2016, doi: 10.1016/j.jhydrol.2016.03.017.
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