Inundation depth prediction device, and inundation depth prediction method

An inundation depth prediction device comprising:

• flow speed value acquiring circuitry to acquire a flow speed value on a sea surface;
• and
• inundation depth predicting circuitry to predict an inundation depth on a ground by inputting the flow speed value acquired by the flow speed value acquiring circuitry to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface,
• wherein the flow speed value acquired by the flow speed value acquiring circuitry is time-series data indicating a flow speed value for each time,
• the learned inundation depth prediction model used by the inundation depth predicting circuitry is a convolutional neural network model, and
• the inundation depth predicting circuitry determines whether or not the time-series data acquired by the flow speed value acquiring circuitry includes a required amount of data for predicting the inundation depth using the learned inundation depth prediction model, and in a case where the inundation depth predicting circuitry determines that the time-series data does not include the required amount of data, the inundation depth predicting circuitry performs complementation of a shortage amount of data on the time-series data acquired by the flow speed value acquiring circuitry.

An inundation depth prediction device comprising:

• flow speed value acquiring circuitry to acquire a flow speed value on a sea surface;
• and
• inundation depth predicting circuitry to predict an inundation depth on a ground by inputting the flow speed value acquired by the flow speed value acquiring circuitry directly to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface,
• wherein the inundation depth predicting circuitry predicts a probability distribution indicating an occurrence probability for each inundation depth by inputting the flow speed value acquired by the flow speed value acquiring circuitry directly to the learned inundation depth prediction model.

An inundation depth prediction method comprising:

• acquiring a flow speed value on a sea surface;
• and
• predicting an inundation depth on a ground by inputting the acquired flow speed value directly to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface,
• wherein a primary prediction value of the inundation depth is predicted by inputting the flow speed value acquired directly to the learned inundation depth prediction model, and a secondary prediction value of the inundation depth is calculated on a basis of the predicted primary prediction value and a past prediction value of the inundation depth predicted in a past.

An inundation depth prediction method comprising:

• acquiring a flow speed value on a sea surface;
• and
• predicting an inundation depth on a ground by inputting the acquired flow speed value to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface,
• wherein the flow speed value acquired is time-series data indicating a flow speed value for each time,
• the learned inundation depth prediction model used is a convolutional neural network model, and
• it is determined whether or not the time-series data acquired includes a required amount of data for predicting the inundation depth using the learned inundation depth prediction model, and in a case where it is determined that the time-series data does not include the required amount of data, complementation of a shortage amount of data is performed on the time-series data acquired.

An inundation depth prediction method comprising:

• acquiring a flow speed value on a sea surface;
• and
• predicting an inundation depth on a ground by inputting the acquired flow speed value directly to a learned inundation depth prediction model used for predicting the inundation depth on the ground from the flow speed value on the sea surface,
• wherein a probability distribution indicating an occurrence probability for each inundation depth is predicted by inputting the flow speed value acquired directly to the learned inundation depth prediction model.