Rapid Deep Learning-Based Flood Losses/Risk Prediction Tool, Methods of Making and Uses Thereof

A computing system for flood prediction, the computing system comprising:

• a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface;
• the processor configured to:
• synchronize a plurality of weather events to compute associated input-output pairs of rainfall sequences and flood characteristics;
• train a set of candidate deep learning models using the associated input-output pairs of rainfall sequences and flood characteristics, to output a plurality of trained inundation-depth-estimating deep learning models and a flood-extent-predicting deep learning model;
• average the plurality of trained inundation-depth-estimating deep learning models to generate a plurality of averaging-based inundation-depth-estimating deep learning models;
• output an integrated deep learning model comprising the plurality of average-based inundation-depth-estimating deep learning models and the flood-extent-predicting deep learning model;
• process a new set of rainfall sequences using the integrated deep learning model to output a prediction associated with a specific instance of time, the prediction comprising an inundation depth estimation and a flood extent prediction.

A computing system for flood prediction, the computing system comprising:

• a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface;
• the memory storing at least an integrated deep learning model comprising a plurality of inundation-depth-estimating deep learning models and a flood-extent-predicting deep learning model,
• the plurality of inundation-depth-estimating deep learning models comprising a plurality of regression deep learning models each one configured to compute a given inundation depth estimation associated with a given instance of time;
• the flood-extent-predicting deep learning model is a classification deep learning model configured to compute a given flood extent prediction associated with the given instance of time;
• the plurality of inundation-depth-estimating deep learning models and the flood-extent-predicting deep learning model are all parallelly connected to each other;
• and
• the processor is configured to process a new set of rainfall sequences using the integrated deep learning model to output a prediction associated with a specific instance of time, the prediction comprising an inundation depth estimation and a flood extent prediction.

A method for flood prediction, the method executed in a computing environment comprising one or more processors, a communication interface, and memory, and the method comprising:

• quantifying a flood vulnerability within a specified area of interest, irrespective of flood event characteristics;
• estimating and mapping a flood hazard probability;
• integrating the flood vulnerability and the flood hazard probability to quantify a flood risk;
• and
• developing a rapid flood risk software tool, stored in the memory, to directly quantify flood risk characteristics using deep learning.

A computing system for flood prediction, the computing system comprising:

• a memory, a communication interface, and a processor operatively coupled to the memory and the communication interface;
• the processor configured to:
• quantify a flood vulnerability within a specified area of interest, irrespective of flood event characteristics;
• estimate and map a flood hazard probability;
• integrate the flood vulnerability and the flood hazard probability to quantify a flood risk;
• and
• develop a rapid flood risk software tool, stored in the memory, to directly quantify flood risk characteristics using deep learning.