The Potential of Generative AI for the Urban Water Sector

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The urban water sector is increasingly turning to AI and deep learning to address the complex challenges posed by growing demographics, climate change, and urbanization. Despite the pressing need, this sector has been relatively slow in adopting these technologies compared to others, primarily due to its conservative nature. However, the recent advancements in generative AI have opened new frontiers for innovation, presenting a crucial opportunity for the urban water sector to accelerate its technological evolution. Expected regulations, particularly from institutions like the European Union, should not be viewed as a hindrance but as a catalyst for enhanced collaboration between academia, industry, and public stakeholders. Such collaboration is essential to finally push the development and adoption of reliable and safe AI systems, ensuring alignment with regulatory frameworks. In this work, we first provide an overview of the latest trends in generative AI, focusing on how Large Language Models and Large Multimodal Models can benefit the urban water sector. Particularly, Large Multimodal Models can offer an added layer of explainability to predictive models working on imagery or other sensor data, a highly desirable feature for applications related to critical infrastructure. By literally asking these models to explain their decision-making processes with respect to the processed data streams, we can partially demystify the 'black box' nature of AI systems. This potential is highlighted for a case study on sewer defect detection, utilizing a Large Multimodal Model that processes both text and imagery. The predictive results on the publicly available SewerML dataset are encouraging with respect to existing deep learning methods. More importantly, we show that explanations provided by the Large Multimodal Model can enlighten the decision-making process, making it more transparent. This added layer of explanation offers valuable insights and may set a new trajectory for developing trustworthy AI methodologies in critical water infrastructure management.