The Solution: XMPro iBOS for Flood Prediction and Response

XMPro’s solution effectively manages flood risks through advanced data integration, predictive analytics, and emergency response planning. It integrates meteorological, hydrological, and geographical data using the Data Stream Designer to monitor flood risk factors. Machine learning algorithms predict flood events, aiding proactive response planning and resource allocation.

The system provides real-time monitoring of weather conditions and water levels, with alerts for utility operators and emergency responders. It also coordinates emergency response efforts, including mobilizing crews, activating flood barriers, and managing water resources. Public safety is enhanced with automated alerts and updates, while configurable dashboards and reporting features support comprehensive post-event analysis and regulatory compliance.

Figure 1: Flood Prediction Data Stream

This flood prediction data stream ingests weather station data to predict potential flooding events. The data is processed through a model path and combined with other relevant data sources. The flood prediction model is then run, and the results are formatted and broadcasted. The data stream generates city flood predictions in both single and chart formats and runs recommendations to provide actionable insights for flood mitigation and response.

Figure 2: Pump Health Condition Monitoring Data Stream

This pump condition monitoring data stream ingests pump telemetry data before cleaning and broadcasting it to various endpoints. The data is contextualized with pump make, model, and operational context. It is then used to calculate pump metrics, post telemetry to ADX, update Azure Digital Twin, and evaluate recommendations.

Figure 3: Pump Predictive Maintenance Data Stream

This pump predictive maintenance data stream ingests pressure, flow, temperature, vibration, and sensor health data from multiple sources before it is normalized and combined with contextual data from SAP and Azure Digital Twin. The integrated data is then used to calculate performance metrics, run predictive models, and update Azure Digital Twin and ADX, enabling the identification of pumps likely to fail and the estimation of their remaining useful life.

Figure 4: Water Pump Unity Model Data Stream

This water pump unity model data stream ingests pump data to monitor and analyze pump conditions. The data is processed and combined with existing recommendations. It is then standardized, broadcasted, and evaluated through the unity pump model. The stream aggregates data by pump assets and stations, incorporates geographical context, and visualizes the data in single and chart formats. Finally, it runs recommendation logic to provide actionable insights for optimal pump performance and maintenance.

Figure 1: Flood Risk Detected – High Recommendation

This example flood risk recommendation identifies a high likelihood of flooding within the next 48 hours for the specified location. It provides event data, including the location (Singapore), date, weather description, current temperature, maximum temperature, and minimum temperature. Users can add notes, mark the recommendation as resolved or a false positive, and create an event response with special instructions if necessary. 

Figure 2. Configure With Granular Rule Logic

Figure 3. Close The Loop On Event Response

Figure 1. Real-Time Flood Prediction and Response 

This advanced dashboard provides water utility operators with a comprehensive, real-time view of their infrastructure, focusing on flood prediction and response. It features an interactive map that dynamically updates with live and predicted precipitation data, flood risk assessments from aggregated sources including meteorological data, Hydrological data, and Geographical Information System data. In addition to these data sources, the real time condition and the condition of various assets such as treatment plants, pump stations, reservoirs, and pipe networks may also indicate flooding.