Automated disaster preparedness planning

Generative AI models like large language models (LLMs) can be pivotal in disaster preparedness planning. These models can analyze historical data, understand patterns, and produce actionable insights. For instance, they could predict incoming weather events by contextualizing past weather patterns and related incidents. Language models like ChatGPT could auto-generate emergency procedures, safety protocols or evacuation plan drafts by training on best-practice documents.

Similarly, generative AI image models could be used for hazard recognition and assessment. For instance, these models can be trained to identify wildfire prone areas or flood risk zones based on satellite imagery. Thereafter, they can create predictive maps for risk management. Furthermore, these models could generate simulations of various disaster scenarios, aiding in creating response strategies or educating the public on the potential effects of unpredictable phenomena.

This is an illustration of a typical application you could build using a tool called Clevis. The example application is an Automated Disaster Preparedness Planner that aims to automate the process of disaster preparedness planning, providing personalized recommendations and resources to users.

Firstly, the user is asked to input their location information. This happens in the Text Input step.

Next, the application implements an Http Request to fetch relevant information about the user's location. This data includes the risk of different types of disasters and historical data about past incidents in the area.

With the response from the Http Request, the app then uses ChatGPT, an OpenAI tool, to generate personalized disaster preparedness recommendations. This is detailed in the ChatGPT step encoding the user's location and risk factors.

Furthermore, this application can also use DALL·E, another AI module, to generate an image illustrating the suggested disaster preparedness kit for the user based on their location and recommendations from ChatGPT.

Lastly, the Display Output step in the application presents the personalized recommendations and the generated image of the disaster preparedness kit to the user.

Applications of similar nature can also be built within the same disaster management area using Clevis.