  Imputation of missing data is a task that plays a vital role in a number of
engineering and science applications. Often such missing data arise in
experimental observations from limitations of sensors or post-processing
transformation errors. Other times they arise from numerical and algorithmic
constraints in computer simulations. One such instance and the application
emphasis of this paper are numerical simulations of storm surge. The simulation
data corresponds to time-series surge predictions over a number of save points
within the geographic domain of interest, creating a spatio-temporal imputation
problem where the surge points are heavily correlated spatially and temporally,
and the missing values regions are structurally distributed at random. Very
recently, machine learning techniques such as neural network methods have been
developed and employed for missing data imputation tasks. Generative
Adversarial Nets (GANs) and GAN-based techniques have particularly attracted
attention as unsupervised machine learning methods. In this study, the
Generative Adversarial Imputation Nets (GAIN) performance is improved by
applying convolutional neural networks instead of fully connected layers to
better capture the correlation of data and promote learning from the adjacent
surge points. Another adjustment to the method needed specifically for the
studied data is to consider the coordinates of the points as additional
features to provide the model more information through the convolutional
layers. We name our proposed method as Convolutional Generative Adversarial
Imputation Nets (Conv-GAIN). The proposed method's performance by considering
the improvements and adaptations required for the storm surge data is assessed
and compared to the original GAIN and a few other techniques. The results show
that Conv-GAIN has better performance than the alternative methods on the
studied data.
