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Using Artificial Neural Networks to Fill-in Missing Annual Peak Flows

机译：使用人工神经网络填写缺失的年峰值流量

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The objectives of this project were to: 1) develop Artificial Neural Networks (ANNs) models to fill-in missing data from the peak annual flowrate records for the Santa Clara River watershed, and 2) compare the ANN results with results from linear regression. The purpose of this work was to provide estimates of the missing data so that a frequency analysis would be more accurate in predicting various flood events. The Santa Clara River Watershed in Southern California has a total drainage area of approximately 1,630 square miles. Peak annual flow data was available for nine stations throughout the watershed since 1933. Eight of the nine stations had some missing data. Each station was modeled and inputs to the ANN model consisted of: peak flow from nearby stations, precipitation data, and temporal data. Inputs were studied to determine their importance to modeling success and either kept or discarded. Sensitivity analysis was conducted to help determine the input parameters: one to four neighboring station peak flows, 10-day precipitation data, and the year. While it is easy to understand the explicit relationship between missing streamflow and neighboring station peak flows as well as precipitation data, the relationship between missing streamflow and the year is implicit. This might reflect the changes of watershed characteristics (landuse, regulated dams, etc.) over time and possibly the long-term change of weather patterns. Model characteristics (number of nodes and layers, transfer functions, data pre-processing methods, number of epochs, etc.) were also studied to optimize the ability of the ANN to learn relationships between the inputs and the peak flow. In general: the models performed well with one to four neighboring station peak flows, 10-day precipitation data, and the year; and it was common for testing results to be within about 20% of the target. Linear regression using the same data sets was also performed. The ANN models had a sum of the squared error value 2 to 400 times less than the respective linear regression models.
机译：该项目的目标是：1）开发人工神经网络（ANNS）模型，从Santa Clara River流域的峰值年度流量记录中填写缺失数据，2）与线性回归的结果进行比较。这项工作的目的是提供缺失数据的估计，使得频率分析在预测各种洪水事件方面更准确。 Santa Clara Riverhed在南加利福尼亚州的排水区约为1,630平方英里。自1933年以来，在整个流域的九个站中可获得峰值年度流量数据。九个站中的八个有一些缺少的数据。每个站被建模并输入到ANN模型中由：来自附近站的峰值流，降水数据和时间数据。研究了输入以确定它们对成功和保存或丢弃的重要性。进行了灵敏度分析，以帮助确定输入参数：一到四个相邻站峰值流动，10天降水数据和年份。虽然很容易了解丢失的流流和相邻站峰值流量之间的显式关系以及降水数据，但缺失流流与年之间的关系是隐式的。这可能会反映流域特征（土地使用，受管制水坝等）的变化随着时间的推移，并且可能是天气模式的长期变化。还研究了模型特征（节点和层数，传递函数，数据预处理方法，时期的数量等），以优化ANN学习输入与峰值流动之间的关系的能力。一般：模型与一到四个相邻站峰值流量良好，10天降水数据和年份;它是常见的，测试结果在目标的约20％内。还执行使用相同数据集的线性回归。 ANN模型的平方误差值的总和比相应的线性回归模型小于400倍。

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