  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.
