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Data will play an invaluable role in helping public and private sector stakeholders across Africa identify ways to increase access to water for all while managing this commodity . From climate and weather forecasting to water monitoring , measuring , demand forecasting , and predictive maintenance on infrastructure , understanding data becomes a key tool in accomplishing water access .
Take disaster management as an example . Municipalities are at risk of flooding from both natural and unnatural events . Whether it is rain , tidal surges , or water main breaks , the complexities of being prepared for floods in the event of a disaster are significant . Further compounding this is climate change , cloud seeding , population growth , concretisation of the urban areas with poor drainage systems and expanding urbanisation in rural areas .
Flood risks in urban areas pose significant challenges , leading to severe losses in terms of human lives , infrastructure , vehicles , and biodiversity . These events can cause widespread devastation , disrupting communities and economies . The impact extends beyond immediate damage to buildings and roads , affecting ecosystems and natural habitats as well .
Machine learning and Artificial Intelligence techniques can forecast potential flooding incidents , identify risk zones , attach risk scores to properties , infrastructure , and vehicles , and notify response teams proactively . This data can be incorporated into a mobile application integrating real-time decisioning with historical data , alert notifications , predictive summaries , and key statistics , aiding municipalities and local city administration in optimising their action plans .
Satyajit Dwivedi , Regional Director , EMEAP , Energy Utilities , Mining and Metals , Public Sector , SAS
Addressing flood risk in cities requires comprehensive strategies that prioritise both human safety and environmental resilience , emphasising the importance of proactive planning , infrastructure improvements , and sustainable development practices .
Cloud and AI
Leveraging cloud-based AI technology with sensor data and drone camera feeds presents a promising avenue for mitigating various flood-related risks . By facilitating efficient flood response management , this approach enables municipalities to better address the challenges posed by flooding . Solar-powered sensors and drones , coupled with cloud-based predictive analytics , offer real-time spatial situational awareness , allowing authorities to monitor flood severity and hazard indicators promptly .
Use cases
In the Netherlands , the agency in charge of Dutch public works , roads and water management is placing advanced IoT sensors throughout the country ’ s numerous rivers and inlets monitoring data from bridges , dams , dikes , locks and storm surge barriers . This is empowering the department to harvest , analyse , and act on real-time insights generated from AI and advanced analytics connected to these sensors .
Application of AI helps them understand how many miles of highways are still operational due to rising water level and plan for adequate sand inventory at the required site .
Critical data , including damage extent , flood depth , flooding arrival time , flooding duration , sediment or contamination load , water height , and flow rates , are transmitted back to the cloud via cellular communications . This telemetry and video feed are then integrated with weather data and predictive models are built that decides a specific response , enhancing the overall flood monitoring and response capabilities .
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