基于QPE和QPF的遗传神经网络洪水预报试验

以湖北省清江上游水布垭控制流域为例，利用分组Z-I关系并结合地面雨量站资料对雷达估算降水进行校准，计算出流域实况平均面雨量；再利用遗传算法和神经网络相结合的方法建立订正AREM预报降水的模型；最后，将订正前后的AREM预报降水输入新安江水文模型进行洪水预报试验。结果表明：订正后AREM预报降水能明显提高过程的累计降水量预报精度，平均相对误差减小幅度在60%以上，对逐小时过程降水预报精度也有一定提高，但与实况相比仍有一定差距；订正前后AREM预报降水的洪水预报试验的确定性系数的场次平均从-32.6%提高到64.38%，洪峰相对误差从39%减小到25.04%，确定性系数的提高效果优于洪峰相对误差，整体上洪水预报精度有所提高。

The preliminary experiment of genetic-neural network flood forecasting based on QPE and QPF

Taking the Shuibuya control watershed in the upstream of Qingjiang in Hubei Province as an example, in this study we first use grouped Z-I relationships and radar precipitation estimates calibrated by data from surface meteorological stations to calculate the area aver-aged precipitation of the watershed. Then, genetic algorithms and neural networks method are combined to establish a revised AREM precipi-tation forecasting model in order to improve forecast accuracy of AREM precipitation. Finally, AREM precipitation data before and after ap-plying the revised model are inputted to the Xinanjiang hydrological model to examine the accuracy of the flood forecasts. Results show that the revised AREM precipitation forecasting model can significantly improve the forecast accuracy of the event cumulative precipitation. The averaged relative error reduction rate is more than 60%. Hourly precipitation forecast accuracy is also improved to some extent, although there is still some bias compared to actual observations. The averaged flood forecast deterministic coefficient of the AREM precipitation forecast by using the revised model is improved from-32.6%to 64.38%, peak relative error is decreased from 39%to 25.04%. The improvement to the deterministic coefficient is better than that to the peak relative error. The overall flood forecast accuracy has generally improved.