Hello everyone! Today, I want to share some insights from my recently published article on the design of a Hybrid Decision Support System (HSWD) for fire services, specifically for the State Fire Service (PSP) in Poland. The goal of this system is to assist rescue teams in making better, faster, and more informed decisions during emergency situations. Let’s break it down in simple terms!
What’s the Problem?
Currently, the fire service relies on a system called EWID to record incidents. However, this system has some limitations:
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It provides static, statistical models that don’t adapt well to dynamic, real-time situations.
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It doesn’t offer practical guidance during emergencies, such as how to allocate resources or where to direct firefighting efforts.
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There’s a lack of shared knowledge between different fire units, meaning that valuable expertise from one unit isn’t easily accessible to others.
In short, the current system doesn’t provide the real-time decision-making support that firefighters need during critical situations.
The Solution: A Hybrid Decision Support System (HSWD)
To address these issues, we proposed a Hybrid Decision Support System (HSWD). This system combines expert systems (which use predefined rules to make decisions) and case-based reasoning (which learns from past incidents to suggest solutions). The idea is to create a system that can:
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Analyze past incidents to provide recommendations.
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Adapt to real-time conditions during emergencies.
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Share knowledge across different fire units.
How Did We Design It?
We used a methodology called Design for Trustworthy Software (DFTS), which focuses on creating reliable and robust software. Here’s a simplified breakdown of the process:
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Identify the Problem: We observed the challenges faced by firefighters and identified the gaps in the current system.
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Define Requirements: We worked with firefighters to understand what they needed from the system.
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Design the System: We created a model for the HSWD, focusing on minimizing complexity while maximizing functionality.
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Test and Refine: We’re currently in the process of testing the system and refining it based on feedback.
Key Challenges
During the design process, we encountered several challenges:
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Lack of Clear Requirements: Firefighters couldn’t always articulate what they needed, so we had to infer their requirements through observation and interviews.
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Data Complexity: The existing data from the EWID system was unstructured and hard to use for real-time decision-making. We had to develop methods to extract and structure this data effectively.
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Knowledge Sharing: There was no centralized system for sharing knowledge between fire units, so we had to design a way to distribute expertise across the network.
What’s Next?
The next steps involve:
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Building a Knowledge Base: We’re working on creating a database of past incidents that the system can learn from.
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Testing the System: We’ll be testing the system in real-world scenarios to see how well it performs.
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Refining the Model: Based on feedback, we’ll continue to improve the system to better meet the needs of firefighters.
Why This Matters
This system has the potential to save lives by helping firefighters make better decisions during emergencies. By leveraging past experiences and real-time data, the HSWD can provide actionable insights that improve the efficiency and effectiveness of rescue operations.
Final Thoughts
Designing a system like this is no small feat, but the potential benefits are enormous. By combining technology with real-world expertise, we can create tools that truly make a difference in critical situations. I’m excited to see where this project goes next!