AI-enabled modeling for smart rural wastewater treatment systems: current practices and remaining gaps

Document Type

Article

Publication Date

2-1-2026

Abstract

Artificial Intelligence (AI) offers significant potential to transform wastewater treatment by enhancing reliability, affordability, and sustainability. However, the adoption of AI in rural wastewater management remains limited due to unique challenges, including constrained resources, fragmented infrastructure, and variable water quality. These issues significantly impede the effectiveness of wastewater treatment, intensifying environmental pollution and public health threats in rural communities. This review systematically analyzes literature published between 2006 and 2024 on AI-driven wastewater monitoring and management, emphasizing machine learning (ML) and deep learning (DL) techniques tailored for urban and rural contexts. Relevant peer-reviewed studies were identified using targeted keyword searches across ScienceDirect and Elsevier databases, prioritizing comprehensive methodology and transparent reporting. Findings demonstrate that existing AI approaches predominantly address urban wastewater systems by optimizing chemical usage, energy efficiency, and operational effectiveness. Conversely, rural systems continue to face barriers such as data scarcity, incompatible infrastructure, and limited interpretability of ML and DL models, hindering AI implementation. To bridge these critical gaps, this paper recommends a modular, interpretable AI framework incorporating hierarchical input decomposition, adaptive data augmentation, and real-time monitoring strategies tailored explicitly to rural conditions. Furthermore, future research directions are also proposed to advance energy efficient, cost-effective, and privacy-preserving federated learning methodologies. Enhancing interpretability, addressing rural-specific data challenges, and promoting collaborative policy frameworks with active community participation are essential steps. Ultimately, scalable AI interventions emphasizing adaptive, interpretable strategies are urgently needed to mitigate environmental risks, safeguard public health, and promote sustainable wastewater infrastructure in rural communities.

Publication Source (Journal or Book title)

Applied Water Science

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