It is important to forecast dam inflow for flood damage mitigation. The hydrograph provides critical information such as the start time, peak level, and volume. Particularly, dam management requires a 6-h lead time of the dam inflow forecast based on a future hydrograph. The authors propose novel target inflow weights to create an ocean feature vector extracted from the analyzed images of the sea surface. We extracted 4,096 elements of the dimension vector in the fc6 layer of the pre-trained VGG16 network. Subsequently, we reduced it to three dimensions of t-SNE. Furthermore, we created the principal component of the sea temperature weights using PCA. We found that these weights contribute to the stability of predictor importance by numerical experiments. As base regression models, we calibrate the least squares with kernel expansion, the quantile random forest minimized out-of bag error, and the support vector regression with a polynomial kernel. When we compute the predictor importance, we visualize the stability of each variable importance introduced by our proposed weights, compared with other results without weights. We apply our method to a dam at Kanto region in Japan and focus on the trained term from 2007 to 2018, with a limited flood term from June to October. We test the accuracy over the 2019 flood term. Finally, we present the applied results and further statistical learning for unknown flood forecast.
翻译:水力测量系统提供关键信息,例如起始时间、峰值水平和体积。 特别是, 水坝管理需要根据未来水力测量对水坝流入量进行6小时的预测。 作者建议采用新的目标流入权重, 以创建从分析过的海面图像中提取的海洋地貌矢量。 我们提取了4 096个维度矢量元素, 用于培训前VGG16网络的fc6层。 随后, 我们将其减少到 t- SNE的三个维度。 此外, 我们创造了使用五氯苯甲醚的海温重量的主要组成部分。 我们发现这些重量有助于通过数字实验实现预测或重要性的稳定性。 作为基础回归模型, 我们用最小的内核扩张、 孔随机森林来调整最小方形矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量, 从2007年6月20日到2007年6月30日, 我们用方法对日本的洪水预测进行了有限的测量, 从2007年6月20日至2007年10月20日, 我们从2007年的洪水预测期,从2007年开始, 以未知期, 以未知的深度的洪水测量到2007年1月20号,我们从2007年1月20日的洪水的深度测量到2007年1月1月20日, 我们用到20号的航号的航标号的航号的航量 学习到20号 。