Industry

• Publishing, Media and ICT Industry (JMain Category)
• Information Services Industry (63Subcategory)

Industry Pain Points

• Material Storage and Replenishment: In the event of a disaster, there is a need for a large amount of emergency relief materials, such as food, water sources, medical supplies, and rescue tools. However, effectively storing and replenishing these materials while ensuring their timeliness and appropriateness is a challenge. Determining the types and quantities of materials needed, as well as how to replenish them quickly after a disaster occurs, are problems that need to be addressed.
• Materials Dispatch and Distribution: After a disaster, effectively dispatching and distributing stored materials to affected areas is critical. It is necessary to consider factors such as the needs of the disaster area, severity of the disaster situation, and transportation and communication conditions to ensure that materials are delivered quickly and prioritized for those most in need.
• Risk Assessment and Forecasting: The occurrence of natural disasters often has uncertainties, but through risk assessment and forecasting, preparation can be done in advance and material dispatch strategies can be optimized to deploy relief materials and human resources ahead of time, improving response capabilities.
• Information Management and Coordination: Disaster relief operations involve the participation of multiple organizations and institutions, including government departments, non-profit organizations, volunteers, etc. Effective information management and coordination mechanisms can facilitate information sharing, task allocation, and collaborative coordination, ensuring an efficient operation of relief materials and maximum service coverage.

AI-Driven Benefits

• Increased Efficiency: Artificial Intelligence can automate and optimize the processes for material dispatch and replenishment, rapidly analyze disaster situations and needs, and generate optimal material dispatch plans, improving the timeliness and accuracy of materials. Additionally, AI can promote information sharing and collaboration among various organizations and institutions allowing different units to share real-time data and intelligence, participating together in the decision-making process for material dispatch and replenishment, thus enhancing the overall rescue efficiency.
• Cost Reduction: Using artificial intelligence to assess and predict the likelihood, intensity, and scope of disasters can avoid over-storage or waste; at the same time, it can better plan and manage the storage and replenishment of supplies, making material dispatch more precise and effective, thereby reducing costs of manpower, materials, and transportation.
• Optimized Resource Allocation: Artificial intelligence can accurately calculate material needs of different disaster areas and demand points based on meteorological or geographical data, optimizing resource allocation to ensure that supplies are prioritized to areas and populations most in need, enhancing rescue effects while reducing resource wastage and losses.

Common AI Techniques or Applications

• Convolutional Neural Networks: Utilizing artificial intelligence like convolutional neural networks to process and analyze satellite images of the disaster area, identifying damaged areas and routes, helps in assessing the situation in the disaster area and planning the allocation of rescue resources.
• Long Short-Term Memory Networks: Utilizing artificial intelligence like long short-term memory networks to analyze time-series data, such as historical disaster data and meteorological changes, to predict future trends in material needs, thus optimizing the allocation plans for materials and rescue personnel.

「Translated content is generated by ChatGPT and is for reference only. Translation date：2024-05-19」