Changes Approaching for the Water and Wastewater Sectors: AI Webinar Insights
Like most industries, the water and wastewater sectors are finding machine learning or artificial intelligence (AI) helpful to address challenges, improve efficiency, and transform operations. A recent webinar hosted by the American Water Works Association (AWWA) delved into the practical applications of AI in the water industry, featuring expert insights and real-world examples. AWWA declared that AI and machine learning have huge implications for the future of water, and Walden agrees. Below we discuss key takeaways from the webinar, exploring the potential of AI to revolutionize the water sector.
AI in Action: Case Studies and Use Cases
The webinar highlighted several compelling use cases where AI is making an impact in the water sector. One notable example is the application of AI in detecting wildfires in California. The technology has proven to be effective, with 40% of fires detected by AI before a 911 call was received. The success of this application serves as an example for other related or similar AI-driven approaches to prevent leaks in the water system, providing early alerts and estimating the risk associated with aging infrastructure.
Additionally, the concept of multimodal inputs and outputs was discussed. This showcased the versatility of AI in processing voice, image, and text inputs while generating outputs in various formats. The practical applications range from voice-activated commands for data analysis to complex simulations and optimizations for water system management.
Overcoming Challenges and Building a Community of Practitioners
Implementing technologies like AI comes with its share of challenges. One of the webinar presenters, Robert Bornhofen, shared his experience in leading Washington DC Water’s adoption of AI. He emphasized the importance of starting at the middle of the organization, forming a Community of Practitioners (CoP), and convincing key stakeholders to embrace change. By encouraging communication across the organization and adopting policies to manage risks, Bornhofen outlined a comprehensive approach to ensure successful AI adoption.
The CoP model was highlighted as an effective strategy for overcoming challenges. By bringing together 200 interested employees and forming a CoP of 45 people, DC Water created a collaborative environment where individuals from different levels of the water utility could contribute their insights. This approach not only accelerates the development of AI technologies but also promotes a culture of innovation within an organization.
Other Applications of Machine Learning
Walden has been monitoring the efficacy of these predictive technologies for the last few years. In a 2021 article posted on Walden’s blog, BlueConduit, our guest author, identified emerging technologies that can be used for lead service line replacement efforts and save utility providers money. The process uses predictive modeling to identify service lines that are most likely in need of replacement. The outcome of using the AI method is a more focused replacement and rehabilitation program with a higher percentage of investment in actual lead service line removals, rather than potholing, dig-ups, or other more intrusive (and expensive) investigative methods. With more than three years of improving the use of these tools, water utilities are getting results.
The AWWA webinar provided a deep dive into the current landscape of AI in the water sector, showcasing its potential to improve operations, enhance efficiency, and address long-standing challenges. Other innovative ways of using these tools are also under way and proving to be effective. AI is developing into a valuable tool for the industry.