The Solution: XMPro iBOS for Reservoir Water Level Flood Prevention

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: Water Reservoir Level Predictive Management Data Stream

This water reservoir level predictive management data stream ingests current weather station data and historical reservoir water level data. It processes and joins real-time water level data with historical data to create a comprehensive dataset, which is then fed into an ML Flow water level predictive model to forecast future water levels. The data stream continuously monitors if the current water level exceeds predefined thresholds indicating immediate risk and automatically triggers water release procedures through the action agent to lower the water level in critical response situations. The predictive model output is visualized for operational insights, and actionable recommendations are generated based on the predictive data and current conditions, leveraging XMPro’s embedded AI agents and machine learning capabilities to ensure timely and efficient water reservoir management.

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: Reservoir 1 Level Nearing Capacity Recommendation

This recommendation identifies that Reservoir 1 is nearing its maximum storage capacity. It provides critical event data, including the facility name (Reservoir 1), ID (24), type (reservoir), facility code (RES01), and current flow rates in and out (both at 0). Users can add notes, mark the recommendation as resolved or a false positive, and create an event response with special instructions if necessary. This proactive alert helps manage water levels efficiently, preventing overflow and ensuring optimal reservoir operation.

Figure 2: 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 3. Configure With Granular Rule Logic

Figure 4. Close The Loop On Event Response

Figure 1: Embedded AI Example – Flood Prediction

Embedding XMPro AI Agents in XMPro Data Streams enables executable AI and machine learning for algorithmic business processes, significantly enhancing the capabilities of operational digital twins. This integration allows for advanced features such as real-time analytics, MLOps, and the seamless embedding of AI into core business processes.