基于正则化GRU模型的洪水预测

针对传统神经网络模型在洪水预测过程中存在准确性低、过拟合等问题，本文以赣江流域外洲水文站每月平均水位为研究对象，提出基于正则化GRU神经网络的洪水预测模型来提高洪水预报精度.选用relu函数作为整个神经网络的输出层激活函数，将弹性网正则化引入到GRU模型中，对网络中输入权重w实施正则化处理，以提升GRU模型的泛化性能，并将该模型应用于外洲水文站每月平均水位的拟合及预测.实验对比表明，弹性网正则化优化后的模型预测拟合程度较高，合格率提高了9.3%，计算出的均方根误差较小.

Flood Forecast Based on Regularized GRU Model

Aiming at the problems of low accuracy and over-fitting of traditional neural network model in flood forecasting process, this study takes the monthly average water level of Waizhou Hydrological Station in Ganjiang River Basin as the research object, and proposes a flood forecasting model based on regularized GRU neural network to improve the accuracy of flood forecasting. Relu function is selected as the output layer activation function of the whole neural network. To improve the generalization performance of GRU model, regularization of elastic network is introduced into GRU model, and regularizes the input weights in the network. The model is applied to the fitting and prediction of the monthly average water level at Waizhou Hydrological Station, and the experimental comparison shows that the model optimized by regularization of elastic network has a higher fitting degree, the qualified rate is increased by 9.3%, and the calculated root mean square error is small.