Converging Human Intelligence With AI Systems to Advance Flood Evacuation Decision Making

The powers that artificial intelligence (AI) has developed are astounding, with recent success in integrating into a human cognitive workflow. AI will attain its full potential only if, as part of its intelligence, it also actively teams up with humans to co-create solutions. Combining AI simulation with human understanding and strategic abilities through data convergence may optimize the process and provide a capacity akin to "teaming intelligence." This thesis will introduce the concepts of Human AI Convergence (HAC) capabilities for flood evacuation decision-making. The concept introduced in this thesis is the first step toward the HAC concept in weather disaster applications. This research demonstrates a synergy between humans and AI by integrating the data produced by humans through social media with an AI system to enhance a flood evacuation decision-making problem. The prediction from Long short-term memory (LSTM) and a river hydraulic model, i.e., Height Above Nearest Drainage (HAND), is integrated with human data from X (previously Twitter) to visualize flood inundation areas, which acts as a 3rd party agent for a HAC system. The goal is to synthesize and analyze HAC competence in flood evacuation emergency management and harness the full potential of AI as a partner in real-time planning and decision-making. This thesis has explored why HAC intelligence is essential to emergency planning and decision-making, providing a general structure for researchers to use HAC to devise effective systems that cooperate well and evaluate state-of-the-art, and, in doing so, providing a research agenda and a roadmap for future flood evacuation emergency management, rescue, and decision making. This state-of-the-art flood evacuation product stands to advance the frontier of human-AI collaborative research significantly.