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Home » Industry News » Water Engineering & Management » Using data to increase access to water for shared prosperity in Africa

Using data to increase access to water for shared prosperity in Africa

Satyajit Dwivedi, regional director, EMEAP, energy utilities, mining & metals, public sector, SAS

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. 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. 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.

Leveraging cloud-based artificial intelligence (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. 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.

Machine learning and AI 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.

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