Aqua Safe

An AI enabled quasi-real-time water quality monitoring for early chemical and/or bio-contamination detection

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About The Project

AI enabled quasi-real-time water quality monitoring for early chemical and/or bio-contamination detection is a groundbreaking project aimed at addressing UN’s Sustainable Development Goal (SDG) #6, which focuses on ensuring universal access to clean water and sanitation. Despite being a fundamental requirement for human existence, over 2 billion people worldwide lack access to uncontaminated drinking water, leading to grave health risks. The prevalence of contaminated water sources is responsible for the transmission of severe diseases, such as cholera, typhoid, and polio, posing a significant threat to public health. Even areas with proper water treatment facilities are susceptible to contamination due to breaches in distribution systems. This project endeavors to revolutionize water quality management through an innovative AI-enabled IoT solution.


17 months (March 2022 to February 2023)​

Grant Amount​

149,965 EUROs​

Funding Agency​

TEINCC - Asi@Connect​


The primary objective of the project is to develop an AI-enabled IoT-based early warning bio-surveillance system capable of detecting and predicting contamination events in freshwater body reservoirs. The project aims to assess the risk associated with consuming the water, thus providing timely warnings to prevent potential health hazards

Target Audience

Water Authorities

Municipalities, water distribution services providers, and water management organizations will gain access to real-time insights, enabling them to maintain high water quality standards and respond promptly to contamination risks

Educational Institutions

The project will engage with academia, specifically researchers, engineers, and students, fostering knowledge exchange and capacity building in IoT and AI technologies

Humanitarian Organizations

Collaborative efforts with organizations like Action Caring Team Malaysia will extend the reach of the project's impact, enhancing the well-being of underprivileged communities

Relevance to UN SDGs

Improved water quality directly influences food safety and agricultural practices, contributing to achieving zero hunger

Timely detection of water contamination minimizes health risks and curbs the spread of waterborne diseases, promoting good health and well-being

The project's core focus on ensuring clean water aligns directly with SDG 6, promoting safe and sustainable water access

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The project creates employment opportunities and capacity building, contributing to decent work and economic growth

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Enhanced water quality management contributes to creating sustainable and resilient cities and communities

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Through efficient water resource management, the project indirectly supports climate change mitigation efforts

Project Implementation

The project's implementation comprises several key components:


Water Quality Monitoring

The project employs IoT sensors to measure physical, chemical, and microbiological water quality parameters, ensuring accurate and real-time data collection

OIP (1)

AI-based Contamination Detection

An AI model trained on cloud infrastructure analyzes the collected data to predict and detect contamination events, enabling timely response

OIP (2)

Dashboard Visualization

A user-friendly web-based dashboard, powered by Spark streaming analysis and Tableau software, provides a comprehensive view of water quality parameters and contamination risks


Capacity Building

The project fosters skill development among technical resources through workshops, training events, and collaboration with academia and NERN partners

Expected Outcomes

Real-time Water Quality Monitoring

The AI-enabled solution ensures continuous and quasi-real-time monitoring of physical, chemical, and microbiological water quality parameters

Early Warning System

The project's early warning system aids in detecting contamination anomalies, minimizing health risks and potential damages

Improved Quality of Life

By reducing laborious water distribution tasks, the project empowers women, supports education, and indirectly contributes to SDG 1, 2, and 5

Sustainable Water Management

The project promotes efficient water resource management, aligning with SDG 6 and contributing to climate change mitigation efforts

Participants & Beneficiaries


Dr. Arsalan Ahmad

Co-Principal Investigator
Associate Professor, NUST-SEECS

Dr. Muhammad Moazam Fraz

Principal Investigator
Professor and Head of Department of AI and DS at SEECS, NUST.

Dr. Yasir Faheem

Co-Principal Investigator
Associate Professor, NUST-SEECS

Project Activity: Trainings

Training Workshop

Training Event
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Training Symposium

AI in Remote Sensing for Precision Agriculture
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Training Workshop

Enabling Sustainable Solutions for Agriculture and Water Resources

Project Activity: Visits and Presentations

Visit to MYREN Network Operation Center
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APAN 55 Attendance and Presentation
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Visit to University of Malaya
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APAN 56 Attendance and Presentation
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The AI enabled quasi-real-time water quality monitoring for early chemical and/or bio-contamination detection project embodies a holistic approach to water quality management, leveraging cutting-edge technologies to ensure safe and sustainable water distribution. By aligning with multiple UN SDGs and fostering collaboration among nations and organizations, the project aims to make a lasting impact on public health, economic growth, and environmental sustainability. Through its innovative AI-enabled solution, the project is poised to revolutionize the way water is managed, distributed, and monitored, ultimately bringing us closer to achieving the vision of clean water and sanitation for all.