Why emergency forecasting is held back by fragmented data
Emergency forecasting rarely hinges on model quality. Far more often, it hinges on the fact that data on floods, fires, and industrial incidents sit in different departmental silos, and each silo is responsible only for its own slice of hazardous phenomenon monitoring.
Update frequency differs by orders of magnitude: a hydrological gauge station reports water level once an hour, a weather station once every ten minutes, radar and satellite images arrive in batches once a day, and some information is still transmitted by phone message or manually copied from a log.
Formats also do not reconcile with one another: raster images, observation tables, sensor telemetry, text summaries. Agencies have their own coordinate systems, object directories, and units of measurement, and exchange happens in scheduled windows rather than as a stream.
Because of this, data reconciliation becomes a manual operation: an analyst downloads exports, brings them to a common time and place grid, and only then do they enter the model.
Hours go into the stitching, and for a flash flood or a crown fire with strong wind, there is no such time — the summary is ready when it is already too late to respond.
The second cost is quality. An emergency forecasting system learns on shifted and incomplete series: gaps in observations, different time stamps, coordinate discrepancies.
The trigger threshold is blurred, and early warning turns from a signal into a stream of clarifying requests.
Therefore this hinges not on the algorithm but on the data collection loop: a single time, unified directories, streaming transmission instead of scheduled exports. We break down how this is addressed.
What changes after implementation: response speed and assessment accuracy
Before implementation, the path from an indicator to notification passed through the duty officer: they reconciled readings from several panels, checked them against regulations, and only then raised an alarm. The review took tens of minutes, and during that time the event had time to develop.
After implementation, the model evaluates the stream continuously — response time to a threat is reduced to minutes, and the duty officer receives not raw data but a ready assessment with a list of the indicators that caused it.
Forecast accuracy grows not by itself but through feedback: every confirmed and refuted event returns to the training loop. The share of missed events falls, but the key effect is different — false alarms decrease.
When the system alerts rarely and for good reason, the operator stops ignoring it, and it was precisely habituation to constant false signals that was the main source of misses.
A separate class of failures disappears entirely — losing an episode when the channel to a data source breaks. A buffer at the collection node and reprocessing after communication is restored prevent the gap from turning into a blind spot.
Emergency preparedness ceases to be a declaration: it is measured by recovery time after a failure and the share of scenarios tested on historical data.
We keep the methodological framework according to ISO 22320 — unified requirements for response and information exchange between services. This gives compatibility of reports with external agencies to which data is transmitted during a real event.
Formats of emergency forecasting systems: three working approaches
Forecast mode is not a property of the model but an agreement on how often the system recalculates the estimate and where it gets data from. It determines storage requirements, tolerances for telemetry gaps, and the delay between an actual deviation and a warning. Let us break down three schemes from which teams choose at the start.
The choice is dictated by data maturity and speed requirements. If long observation series have been accumulated and decisions are made by shifts and days, scheduled calculation is enough.
If sensors provide telemetry continuously and delay is costly, streaming evaluation is needed. A hybrid scheme combines both loops — the price is maintenance complexity.
| Variant | When it is suitable | Limitations |
|---|---|---|
| Scheduled calculation | Long historical series and risk zone maps have been accumulated; the forecast is updated on a schedule — once per shift or per day | Does not catch rapid dynamics: a gust, ignition, sharp rise in level. The delay equals the calculation period |
| Streaming evaluation | Data arrives continuously: water levels, weather stations, seismic data | Sensitive to gaps and sensor noise; a calibration failure causes false alarms |
| Hybrid scheme | Both a background forecast and early warning of an anomaly are needed | Two loops, rules for reconciling results, a unified event log, more complex operation |
In practice, we start with scheduled calculation and separate the streaming loop into a separate module — then switching between modes does not require rewriting the system.
Implementation stages: from source audit to handover to operation
We fix the work sequence before the start: a mistake here is costly in both directions — a missed event and a false alarm after which the shift stops trusting the system. We go from data to model and only then to people and regulations.
- Source data audit. We analyze what already exists: sensor telemetry, meteorological series, incident logs. We look at arrival frequency, completeness, gaps, and delays — the forecast horizon depends on this.
- Solution design. We determine which values we forecast, for what period ahead, and at what threshold a signal is issued. We agree before development so that the model does not turn out to be accurate but useless to the duty officer.
- Data preparation. We bring series to a single time scale, restore gaps, label historical events, and balance rare classes — otherwise the model learns on skew.
- Testing on historical events. We run the solution on past episodes and look not at average accuracy but at the share of misses and the number of false alarms. We analyze discrepancies: where data was insufficient, where the threshold interferes.
- Shift training. We show duty officers how the assessment is read, what to do when it triggers, and how to record disagreement with the forecast — this feedback goes into retraining.
- Handover to operation. We hand over documentation, the update regulation, and quality monitoring, and describe behavior when a source changes or disappears.
After handover, we remain on support: we track model degradation and update it according to the regulation, not after a missed event.
Composition of the delivered system: configurations, schemes, regulations
Handover is not a folder of reports but a working loop on the customer's infrastructure. Everything is deployed from version-controlled sources and runs without our involvement: acceptance includes a run on a control dataset with a result that matches the reference.
- System configuration — model parameters, trigger thresholds, aggregation windows, binding to sources; changed without code edits.
- Data flow schemes — the path from sensors and external feeds through the queue and feature calculation to the notification channel, with control points at each stage.
- Access rules — roles and visibility scopes: who works with raw data, who sees only summaries, who confirms an alarm.
- Model decision log — which indicators triggered the alert, with what confidence, who responded and when.
- Response regulation — operator actions at each signal level, escalation procedure, and steps that must be recorded.
- Shift instructions — analysis of typical false alarms and procedures for degraded input data.
- Update regulation — how to retrain the model on new data, test it, and roll back the previous version.
Documentation lives in your repository next to the source code, so new engineers onboard using internal materials rather than correspondence with us.
Case study: flood scenario — metrics before and after implementation
The customer is a territorial service responsible for warning the population in a flood-prone basin. Before the project, flood threat was assessed by water levels at observation posts: the summary arrived once a day, and the lead time did not exceed 12 hours.
That was enough to reinforce the duty shift, but not to set up temporary accommodation points and evacuate people from low-lying areas.
The forecasting case was built around three layers: a runoff model for the catchment, a gridded precipitation forecast, and satellite soil moisture estimates.
Instead of a single scenario, an ensemble is calculated — the duty officer sees the probability distribution across gauging sections and understands which area will flood first.
Open hydrological data GloFAS provided initial conditions for catchments where there are no local gauges.
Metrics before and after: the warning horizon grew from 12 to 72 hours, the share of missed flood events fell by more than three times, and false alarms decreased by about half — shifts stopped responding to every jump in level.
Time from data arrival to ready warning shrank from several hours of manual verification to 15-20 minutes of automatic calculation.
Limitations remained honest: in small mountain catchments with a sparse sensor network, accuracy is lower, and the model predicts ice-jam water level rises worse than rain-driven ones.
The decision to introduce a regime is made by a human — the system provides probabilities and time to peak, not an order.
What to do if sensor data is incomplete and noisy?
The equipment is in place, but some channels are silent: somewhere a communication channel is lost, somewhere a sensor is flooded, and some readings contradict each other.
Raw data in this form cannot be fed to the model — it will train on artifacts and produce a confident but incorrect forecast. Therefore the task is not "to make the stream clean" but to make the forecast robust to gaps in readings.
Data cleaning begins with gap filling: short gaps are closed by time interpolation, long ones are marked as missing rather than filled with a guess.
In parallel, we filter outliers using a rolling median and check readings for physical consistency — water level does not change faster than runoff and terrain allow.
Next, we assess signal quality for each source: consistency with neighboring sensors, time since calibration, dropout rate. A channel with persistent discrepancy receives a lower weight, and the model relies on the others rather than an average across all.
To keep the model from depending on a single channel, training uses masking: in some examples, sensors are forcibly disabled, and the system learns to build a forecast from an incomplete set.
We output the result as an interval with an uncertainty estimate — when a channel drops out, it widens, and the duty officer sees that the support has become thinner.
Separately, we monitor data quality: the share of gaps, discrepancies between sensors, and the number of restored values are visible to the duty officer.
Signal degradation becomes noticeable days before the forecast starts to err — and repairs are prioritized: first the channels that affect the decision.
Limitations and scope of responsibility: what the system will not predict
An emergency forecasting model learns from history, so its main limitation is rare events. They are few by definition: the rarer the scenario, the fewer examples in the sample and the lower the forecast reliability for it.
We do not hide this behind average metrics — we calculate quality separately for each event class, and we follow CRISP-DM from problem definition to model monitoring after launch.
The second limit is source data. Gaps in telemetry, delayed arrival, sensor replacement, or a change in export format shift the estimate, and the system must show this rather than smooth it over.
Therefore we fix a list of critical sources and rules for behavior with incomplete input: the forecast is either marked as unreliable or not issued at all.
Responsibility for the decision remains with the human. The system provides probability and risk level, while the response regulation — who, at what threshold, and what exactly they do — is agreed and documented separately, before commissioning.
What if there is no historical data at all?
Then we rely on expert rules and the physics of the process rather than statistics. Forecast reliability for rare events in this mode is marked as low, trigger thresholds are raised, and the system works as an advisor rather than a source of automatic commands.
What to do if the forecast did not come true?
We analyze each such case: check the completeness of input data, see whether the signal was below the trigger threshold, and adjust settings. Responsibility for the response decision remains with the duty shift — the system only increases the justification of that decision.
Let's discuss the task: what is needed for assessment and work plan
Defining the task for an emergency forecasting system begins not with the model but with answers about data, horizon, and response regulations. Below are inputs sufficient for us to return with a project assessment, implementation scope, and solution design.
Some items are usually clarified during the review, so missing data does not block the conversation.
- What historical series are available: incident logs, telemetry from observation posts, weather and hydrological summaries — with what time step and over what period.
- Forecast horizon and frequency: how many hours in advance a warning is needed and how often the estimate is recalculated — on a schedule or on sensor events.
- Territory and incident classes: a point object, district, or distributed network, and what exactly counts as a trigger.
- Cost of error in both directions: how critical a missed event is and how many false alarms the decision-making shift can tolerate.
- Where the result goes: dispatcher console, mobile notification, adjacent system — and in what form the signal is needed.
- Loop constraints: where data may be stored and processed, whether operation without an external network is required, what access is provided.
- Who confirms quality: the person responsible for the subject area and the episodes on which we test the system.
Once this is collected, we fix the problem statement, run the forecast on past events, and return with a timeline estimate and a stage-by-stage work plan. Describe the task in free form — we will clarify the rest with questions.
