Multi-scale flood prediction based on GM (1,2)-fuzzy weighted Markov and wavelet analysis - Jinping Zhang; Yuhao Wang; Yong Zhao; Hongyuan Fang (Journal of Water and Climate Change, 2021)
In order to forecast flood accurately and reveal the relationship between rainstorm and flood at the micro level, a model which combines wavelet analysis, GM (1,2) and fuzzy weighted Markov is built. Taking the Jialu River of Zhengzhou City in China as study area, the GM (1,2) model is constructed between the components of rainfall and flood volume by wavelet decomposition to connect the two variables, then a fuzzy weighted Markov method is introduced to correct the predicted component of flood volume. The corrected results are superimposed to obtain the predicted value of flood. To verify the reliability of the model, the maximum daily, 3-, 5- and 7-day flood volume of the next five floods in Zhongmu and Jiangang hydrological stations are predicted in turn. The results show that the multi-scale flood forecasting model has high overall forecasting accuracy, with the average relative errors all less than 10%. The forecasting accuracy of maximum five-day flood volume is higher than other periods. On the micro level, the results indicate that the fluctuation trend and period of rainfall-flood volume in d1, d2 and d3 are basically the same. Among the components of forecasted flood, the impact of rainfall on flood volume is most significant in the d3 component. HIGHLIGHTS Combining GM (1,2) with wavelet analysis can reveal the relationship between rainfall and flood volume at the micro level, so as to better reflect the physical mechanism between them.; The forecasted flood volume is reflected not only at the macro level but also at the micro level.; Using the fuzzy weighted Markov method to correct the predicted components, then the prediction model has a favorable prediction effect.;