RAINFALL FORECASTING IN SPACE AND TIME USING A NEURAL NETWORK
RAINFALL FORECASTING IN SPACE AND TIME USING A NEURAL NETWORK
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DOI：
10.1016/0022-1694(92)90046-x
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发表时间：
1992-08-15
影响因子：
6.4
通讯作者：
CUYKENDALL, RR
中科院分区：
文献类型：
--
作者：
FRENCH, MN;KRAJEWSKI, WF;CUYKENDALL, RR
提出了一种用于降雨强度时空预报的神经网络，它是一个由输入层、隐含层和输出层组成的三层学习网络。训练是使用反向传播进行的，其中输入和输出降雨场以一系列学习集的形式呈现给神经网络。训练完成后，用神经网络预测雨强场，提前时间为1h，只用当前雨强场作为输入。降雨场是利用时空数学降雨模拟模式生成的，并将预测场与完全已知的模式产生场进行比较。结果表明，神经网络能够学习描述降雨时空演变的复杂关系，如复杂降雨模拟模型所固有的关系。一个小时前产生的预报，与真实平均面积强度和面积覆盖率的比较表明，在大多数情况下，该方法在应用于训练中使用的项目时表现良好。神经网络被用来预测训练数据中没有包括的一系列事件，并且在利用相对大量的隐藏节点时表现得很好。将神经网络的性能与其他两种短期预测方法--持续性和临时性预测方法进行了比较。
A neural network is developed to forecast rainfall intensity fields in space and time; it is a three-layer learning network with input, hidden, and output layers. Training is conducted using back propagation where the input and output rainfall fields are presented to the neural network as a series of learning sets. After training is complete, the neural network is used to forecast rainfall intensity fields with a lead time of 1 h using only the current field as input. Rainfall fields are generated using a space-time mathematical rainfall simulation model, and forecasted fields are compared with the perfectly known model-produced fields. Results indicate that a neural network is capable of learning the complex relationship describing the space-time evolution of rainfall such as that inherent in a complex rainfall simulation model. One hour ahead forecasts are produced, and comparisons with true mean areal intensities and percent areal coverage indicate that in most cases the method performs well when applied to the events used in training. The neural network is used to forecast a series of events not included in the training data and is shown to perform well when a relatively large number of hidden nodes are utilized. Performance of the neural network is compared with two other methods of short-term forecasting, persistence and nowcasting.