The integration of AI into disaster management brings with it a host of challenges that must be tackled for its effective deployment. These key hurdles encompass:
Hurdles in Incorporating AI into Disaster Management:
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Quality and Volume of Data: A primary obstacle lies in securing high-quality, varied data to train AI models.
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Harmony with Pre-existing Systems: The fusion of AI systems with current technologies and infrastructure can prove intricate and difficult.
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Ethical and Societal Consequences: It is vital to address ethical issues surrounding data privacy, security, and fairness in the use of AI applications.
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Absence of Human Interaction: AI algorithms might lack the human intuition required for complex and unpredictable disaster situations.
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Restricted Adaptability: AI algorithms may find it challenging to adjust to novel and unforeseen disaster scenarios.
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Algorithm Specificity: AI models crafted for particular types of disasters may not be suitable for other disaster categories.
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Expense: The introduction of AI-based solutions in disaster management can come at a high cost.
To surmount these obstacles, investment in research, innovation, and stakeholder collaboration is necessary to fully harness the capabilities of AI in managing disaster risks. Regular assessments, public consciousness, and acceptance of AI systems are also crucial elements that require focus for successful incorporation into disaster management.
