When we talk about “flood monitoring projects,” the image many people have in mind is a dashboard full of water-level graphs and red dots on a map showing “this spot is rising.” But for governors, provincial executives, or budget committees, the key question is rarely “do we have a graph?” – it’s: “How much damage can this system help prevent, and how does it improve people’s readiness?”

This article suggests that a 20–50 sensor deployment should be viewed as a “time management system” rather than just a “graph generator,” and offers a set of metrics (KPIs) that tell a clearer value story for both technical and financial stakeholders.

1. From water-level graphs to measurable “early warning time”

At the heart of any flood monitoring system is the ability to buy time for people in the risk area. The earlier they know, the more calmly they can move cars, lift belongings, or evacuate vulnerable family members. The first KPI you should consider is: Lead Time – the average early warning time before conditions become critical.

Without sensors, information is often based on “what we see with our eyes,” or ad-hoc phone calls and social media posts, which can lag reality by 30–120 minutes (or more). With sensors sampling every 1–5 minutes and clear alert thresholds based on local terrain, you can derive simple but powerful metrics, for example:

  • On average, how many minutes of warning do we get before water crosses a critical level or road becomes impassable?
  • In the last X flood events, how many times did we get at least 30 minutes / 1 hour of early warning?
  • In which cases was the warning “too late,” and how can thresholds or locations be adjusted?

These numbers allow leaders to say in meetings: “Our system now gives us an average of 45 minutes of early warning before roads flood.” That statement is far more meaningful than “We now have real-time graphs.”

2. Response Time: from first alert to first action

A second KPI that matters just as much is Response Time – the time between the first alert and the first operational action. Sensors alone are not enough; someone needs to “catch the ball” and act.

Here are examples of measurable response KPIs:

  • Average time from first alert to the moment officials see it on the dashboard or via Line Notify.
  • Average time from first alert to the first outbound communication to citizens (text message, Facebook post, Line group broadcast, etc.).
  • Ratio between the number of severe alerts and the number of actual public warnings issued – showing whether alerts are truly integrated into operational workflows instead of just staying on a screen.

When tied to existing SOPs (for provincial disaster response, municipalities, or local emergency units), these KPIs become evidence that the “digital system” is not just installed and forgotten, but embedded in real-world decision processes.

3. Go beyond graphs: estimate “damage reduction” in simple terms

For executive and council discussions, the question that follows Lead Time and Response Time is often: “Roughly how much economic damage does this help us avoid?”

We might not be able to calculate a perfect number like in a textbook, but we can build a reasonable order-of-magnitude estimate that supports budget decisions:

  • Estimate average damage per household for a typical flood event (furniture, appliances, vehicles, lost income, etc.).
  • Estimate how many households in risk areas received “timely” warning based on Lead Time & Response Time.
  • Convert that into a rough figure, for example: “With 1 hour of early warning, we estimate that average loss per household drops by X THB. With Y households affected, that’s a potential reduction of Z THB per year.”

Even as a conservative estimate, this shifts the narrative from “the system is important, trust us” to something that finance officers and council members can grasp: roughly how much we’re saving by investing in early warning.

4. Using TCO & ROI in language executives understand

Once you have time-based KPIs and damage estimates, the next step is to frame the project using basic financial concepts: TCO (Total Cost of Ownership) and ROI (Return on Investment).

For example:

  • Sum up all costs over 3–5 years: hardware, connectivity, platform, operations, and maintenance.
  • Compare this with your estimated annual damage reduction. If the system helps reduce annual damage by 5–10 million THB, while the 5-year TCO is 8 million THB, you can confidently state:
    “This means, from a socio-economic view, the system pays for itself within 1–2 years.”

Presenting it this way helps leaders see flood monitoring not as a “cost line item,” but as a long-term resilience investment in safety, public confidence, and continuity of local economies.

5. Connecting sensors to Line Notify, dashboards, and an expansion roadmap

A flood monitoring system only becomes “alive” when data reaches the right people via channels they already use every day – like Line – and when there’s a clear control room or dashboard that owns the overall picture.

Key design points to consider from day one:

  • Line Notify / duty groups – Who gets the first alerts? Who is the backup? Is there a duty officer model or rotating shifts?
  • Central dashboard – Which agency has the province-wide view, and who has the authority to trigger public warnings and inter-agency coordination?
  • Expansion roadmap – Starting from 20 sites, how will you add 10–30 more each year? Are you using historical flood incidents and new urban developments to guide site selection?

When Lead Time and Response Time are tied to concrete processes and tools like Line Notify and a provincial dashboard, you can show that your system covers the full loop: sensors → data → alerts → human decisions → impact measurement.

6. In summary: from “nice graphs” to “valuable minutes” and “defensible budgets”

A 20–50 point sensor network might look small compared to major infrastructure budgets, but with the right KPIs, it can become a flagship example of a digital project whose value is clearly explainable.

To recap the most important elements:

  • Lead Time – how early you can warn before conditions become critical.
  • Response Time – how quickly agencies act after the first alert.
  • Estimated damage reduction – translating minutes of warning into a rough economic impact.
  • TCO & ROI – treating the system as a 3–5 year investment, not just a 1-year cost.
  • Integration – connecting sensors with Line, dashboards, and an expansion plan.

So when a council member or auditor asks, “We installed these sensors – what exactly did we gain?” your answer won’t be “We have better graphs now.” Instead, you can confidently say: “We bought precious minutes of warning for our citizens and reduced the province’s flood damage in a way we can see, measure, and continuously improve.”

Note: This article can be linked to Chonnatee and ESSNext’s Smart Water solutions, and used as a starting point for developing TORs or concept notes for provincial flood monitoring projects.