Artificial Intelligence in Sustainable Wastewater Infrastructure Planning: A Decision-Support Framework for Managing Increasing Complexity
TL;DR
A conceptual framework for decision support in sustainability-oriented wastewater infrastructure planning that brings together three components: data generation, AI-supported analysis, and evaluation with multi-criteria decision analysis is introduced.
Abstract
Wastewater planning in the age of growing complexity is becoming increasingly difficult, calling for the joint consideration of environment-related, economic, technical, and social dimensions under conditions of uncertainty. Existing sustainability evaluation approaches such as life-cycle assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) provide a structured approach to the issue, but are mainly used in well-defined and simplified decision situations, limiting the comprehensive exploration of the system interactions. At the same time, digitalisation, together with artificial intelligence (AI), is transforming wastewater systems into information-rich settings, enabling new kinds of analysis and decision support. Yet, despite the growing use of AI in the wastewater sector, its application for sustainability-oriented infrastructure planning is still poorly conceptualized. This paper introduces a conceptual framework for decision support in sustainability-oriented wastewater infrastructure planning that brings together three components: data generation, AI-supported analysis, and evaluation with MCDA. Specifically, in this framework, the task of MCDA is the clear evaluation of heterogenous indicators, while the added value of AI lies in expanding the analytical horizon with structured and large-scale exploration of the decision space. This reframes the role of sustainability assessment from evaluating a few predefined system configurations to analysing trade-offs, uncertainties, and the robustness of alternative solutions across a wider range of system scenarios. The paper demonstrates the application of the proposed concept in an exemplary analysis of decentralized wastewater system planning. The results highlight the potential and pitfalls of the AI-based analysis and demonstrate that AI does not substitute for other types of assessment but complements them.