Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

中文翻译：

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使用网格降水产品和神经网络模型对降水与水位的关系进行建模

建立降水量与水位关系模型对防洪具有重要意义。近年来，使用机器学习算法进行降水-水位预测在洪水预测和其他领域引起了广泛的关注。但是，缺乏使用网格降水产品和神经网络模型来模拟降水与水位关系的清晰方法。该方法的问题包括如何选择神经网络模型，以及如何以不同类型和分辨率的遥感数据影响建模结果。本文的目的是为这些问题提供一些发现。我们使用反向传播（BP）神经网络和非线性自回归外生模型（NARX）时间序列网络分别对降水和水位之间的关系进行建模。通过两种网络模型，使用三种不同的网格降水产物，对金沙江流域平山水文站的水位进行了模拟。结果表明，当缺少地面站数据时，网格降水量产品可以很好地构建降水与水位的关系。此外，使用NARX网络作为模型拟合网络时，使用额外的输入要比使用BP神经网络更好。前者的纳什效率系数均高于97％，而后者均低于94％。此外，