| dc.description.abstract | Physical scale experiments improve our comprehension of fluvial, tidal,
and coastal processes. However, acquiring accurate, precise, and continuous
data on water depth has been difficult due to the limitations inherent
in the measuring equipment. In this paper, we try to predict water depth
in a two-fold process using different models. First we address the issue of
zero-inflated data. As over 50% of the training data cells were calculated to
contain no water. We aim to develop a binary classification model that predicts
pixels with no water and with water, then use regression to predict
the depth of cells with water.
Binary classification models such as Logistic Regression, Random Forests,
and SVM were compared. These models are trained using overhead imagery
from water depth calibration experiments. The overhead imagery
was overlaid on water depth maps. The water depth maps were calculated
from the calibration experiments using the DEM and increasing weir
heights.
In the second step, regression models were developed and compared. Linear
regression, Random Forests, and SVM models were trained on the pixels
containing water in the water depth maps. The two-step Random Forest
model was selected and applied to different overhead images under
similar experimental conditions, the resulting predicted water depth maps
produced promising results and closely resembled the overhead imagery.
The implication of the experimental data–model integration is that future
experiments can derive water depth from overhead imagery in a simple,
affordable, and labour-efficient manner. | |
