*3.3. Prediction of DBNN Model*

The trained DBNN model can be applied as a classifier for flood monitoring. The flow of flood monitoring is presented in Figure 6a: (1) CYGNSS data are input into the DBNN model to obtain the probability values that the CYGNSS data belong to the submerged area. Specifically, probability values greater than 0.5 are regarded as the submerged areas, while those less than 0.5 are considered as the non-submerged areas. (2) Afterwards, the geolocation of each DDM is realized by using the longitude and latitude of its specular point, and the scatter maps of the predicted results are drawn by combining the prediction results with the location of DDMs. (3) Finally, the scatter maps of the prediction results are gridded into 9 km × 9 km images.
