Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 20 references
TL;DR
This solution presents a scalable, interpretable, and sustainable solution to smart water management, and especially in isolated or underserved areas, this solution will bridge the gap between IoT sensing, AI prediction and practical deployment.
Abstract
Background
The security and safety of water resources is essential to human health, agriculture, and ecosystem. Conventional water
quality monitoring systems are normally based on centralized cloud infrastructures, which cause delays, excessive
consumption of energy, and narrow implementation in remote or poorly resourced regions.
Objective
This paper suggests an explicable TinyML-based edge architecture to monitor water quality in real-time and predictive
analytics. Machine learning model applications can be deployed either on the edge devices directly, which allows sensor data
to be instantly processed and inferred without ongoing reliance on the cloud. Incorporating the use of explainable artificial
intelligence (XAI), the system will determine which parameters the most significantly impact water quality, including pH,
turbidity, and dissolved oxygen, and give transparent information to the user and the decision-makers.
Materials and Methods
The framework helps to monitor outliers and contamination early, and send real-time warning to avert possible risks. The
low-latency performance, energy efficiency, and high predictive accuracy of the system are experimentally assessed and are
superior to the traditional cloud-based methods.
Results
The primary innovation is the integrated focus on TinyML plus edge computing and XAI that have never been additionally
combined in the literature to predict the water quality.
Conclusion
This solution presents a scalable, interpretable, and sustainable solution to smart water management, and especially in
isolated or underserved areas, this solution will bridge the gap between IoT sensing, AI prediction and practical deployment.
Rapid urbanization, industrialization, and climate change have intensified water scarcity, increasing the need for intelligent water distribution systems. Traditional leak detection methods are often slow, labor-intensive, and unable to identify leaks at an early stage, resulting in water loss, higher operational costs, and infrastructure damage. This study proposes an AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, edge-cloud computing, and predictive analytics for real-time leak detection and proactive maintenance. The framework combines intelligent sensing, machine learning, hydraulic analysis, and digital twin technologies to identify pressure, flow, and acoustic anomalies associated with pipeline degradation. Mathematical models are incorporated to estimate leak probability, evaluate sensor reliability, and assess system performance. The proposed architecture enhances detection accuracy, reduces false alarms and non-revenue water (NRW) losses, lowers maintenance costs, and improves infrastructure resilience. Overall, the framework provides a scalable and sustainable solution for intelligent water resource management through continuous monitoring and AI-driven decision support.
Iyengar P.K· International Journal of Eme...· 0 citations
This survey explores recent innovations in IoT-based Water Quality Monitoring Systems (IoT-WQMS) integrated with Machine Learning (ML) and Deep Learning (DL) to enable real-time, automated water quality assessment.
Kumar S. Ashok, Radhakrishnan C. V.· International Journal of Sci...· 0 citations
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Parvathy Krishna V, G. S, Sahala Mehrin et al.· International Journal of Tec...· 0 citations
Rapid urbanization, industrialization, and climate change have intensified water scarcity, creating a need for intelligent water distribution systems. Traditional leak detection methods rely on manual inspections and threshold-based monitoring, resulting in delayed detection, high water losses, increased maintenance costs, and infrastructure damage. This study proposes an AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, cloud-edge computing, and predictive analytics for real-time pipeline monitoring and early leak detection. The framework collects data from pressure, flow, acoustic, and water quality sensors, applying machine learning algorithms to identify hydraulic anomalies and predict leakage probabilities. It also incorporates digital twins and hydraulic simulation models to improve adaptability under varying operational conditions. Mathematical models evaluate leak probability, sensor reliability, and system performance, enabling proactive maintenance and informed decision-making. The proposed architecture enhances detection accuracy, minimizes false alarms, reduces non-revenue water losses and operational costs, and improves infrastructure resilience, supporting sustainable, reliable, and intelligent water resource management.
Suresh Babu Reddy· International Journal of Eme...· 0 citations
Pollution is a growing problem with dire effects on public health; therefore, accurate prediction of Air Quality Index (AQI) is paramount for both urban development and public health strategy. Here, we present GreenAirOps, a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI. This production pipeline has full MLOps pipeline capabilities: Data versioning with DVC; experiment tracking and model registration with MLflow; automated retraining and deployment using GitHub Actions. The deployment infrastructure utilizes Docker containers run on an AWS environ- ment and is designed for production grade. The specific contributions of this work are: an optimized ensemble learning system for low-latency AQI prediction; an end-to-end MLOps system which guarantee reproducibility and operational capability; and a set of production grade functions for auto-retraining, model health monitoring and auto-rollback.
Sahil Goyal, Vaibhav Sharma, Vivek Hotchandani et al.· Air quality, atmosphere and...· 0 citations
Water quality monitoring is rapidly evolving from traditional periodic sampling towards continuous, data-intensive, and intelligent systems. This review examines the current trends in the field of water quality monitoring within the context of big data, and specifically focuses on the intersection of sensor technologies, artificial intelligence (AI), and integrated monitoring systems. Originally, the article examines the current sensing methods, such as electrochemical sensors, optical and spectroscopic devices, biosensors, IoT-enabled networks, remote sensing, and mobile platforms, with an emphasis on their increased monitoring performance and their persistent shortcomings in the areas of fouling, calibration, selectivity, and field performance. Second, it addresses the distinguishing features of water quality big data and highlights the issues of heterogeneity, velocity, uncertainty, preprocessing, data fusion, interoperability, and governance. Third, the paper assesses the use of AI, with special focus on machine learning, deep learning, and water quality assessment, prediction, anomaly detection, and decision support, and critically discusses concerns related to transferability, interpretability, reproducibility, and quantifying uncertainty. Lastly, the article brings together the synthesis of how the intertwining of frameworks of sensing, data infrastructure, analytics, and management platforms can help to transform fragmented observations of water quality into proactive and adaptive control. This review uniquely adopts a systems-level perspective, treating sensors, big-data management, and AI as integrated layers of next-generation environmental intelligence.
Peng Du· Journal of Environmental &am...· 0 citations