What does Digital Water look like in Singapore
Written by Ismail Weiliang and Nasrine Tomasi, Technical Director for Smart Water, New Zealand
ISMAIL WEILIANG
The Waterbender
NASRINE TOMASI
Technical Director, Smart Water
Views are entirely ours
and not connected to any company
The future of water is digital
Singapore is facing significant challenges in the water sector, namely climate change, increasing water demand, rising costs, and ageing infrastructure. This inflicts substantial pressure on Singapore’s water systems. Singapore needs to adapt to meet the essential demands of delivering safe and sustainable drinking water, storm water management and wastewater services within this context.
Addressing these growing challenges in an increasingly changing, complex and uncertain world necessitate the next generation of water systems in Singapore to go beyond the traditional. Digital is disrupting the water sector and its pace and scale is increasing. It is empowering us to derive insights enabling better informed decisions in planning, financing, delivering, operating, and maintaining our existing water systems.
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Quick Take:
The Big 5 Digital Enablers
There are 5 tech enablers identified within the SMART PUB Roadmap, deployed across the entire water sector’s value chain from planning to operations & maintenance. AI: Machine Learning for Decision Support systems. IoT: For Connectivity of sensors and devices. Data Analytics: Harnessing big data for insights. Autonomous Vehicles: Robots or unmanned vehicles to perform manual tasks. Digital Twins: Process Simulations for different scenarios.
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Quick Take:
Digital Water in Singapore
The SMART PUB roadmap directs Singapore's National Water Agency drive to digitalise Singapore's entire water system to improve operational excellence and meet future water needs. Digital twins were developed for drainage, water supply and sewer networks, and
treatment plants in Singapore.
The Smart Drainage Grid has extensive hydrometeorological monitoring and predictions. Flood management can shift from reactive to predictive to prescriptive where we have confidence of what will occur in future events, what is the most effective response is and the outcome of the response. The data can be used for strategic planning of Singapore’s catchment and waterways.
The Smart Plants has optimised operations and decision support with process digital twins. Operations will be man-less with automation.
The Smart Water Grid has extensive monitoring of network pressure, flow, and water quality. It can have pre-emptive leak detection as our networks age. There can be predictive load dispatch with demand forecasting via machine learning.
The Smart Sewer Grid has pre-emptive asset repair and replacement, with the network monitored for structural integrity. Illegal discharges tracing and modelling can also be done to protect our sewers better.
Recently, a coastal-inland flood model is being developed in Singapore. As a small coastal city with many low-lying areas, coupled with the upward trend of average annual rainfall and frequency of heavy rainfall, Singapore is particularly susceptible to coastal and in-land flooding. There is great potential to further develop this model into a coastal-in-land digital twin to support better decision making for our coastal-in-land flood risk management.
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Quick Take:
Authors:
Nasrine Tomasi has 15 years’ experience in water resources engineering. She is currently acting as Moata’s Global Smart Water Product Lead in Mott MacDonald’s Digital Venture team where she drives innovation and digital delivery through water projects. Nasrine has been instrumental in the development and deployment of Mott MacDonald’s digital twin platform Moata which has been leveraged in multiple projects to deliver better insights for decision makers in the water sector.
Ismail Weiliang is a consultant that provides technical advice on climate resilience with half a decade of experience in flood risk advisory for Asia. He also founded "The Climatebender” a non-profit organisation that provides humanitarian relief to communities vulnerable to the climate crisis.
Reference
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