In the heart of Odisha, a groundbreaking study is transforming how we monitor and assess river water quality, with implications that ripple far beyond the banks of the Mahanadi River. Led by Abhijeet Das, a researcher from the Department of Civil Engineering at C.V. Raman Global University (CGU), this innovative work is harnessing the power of artificial intelligence and machine learning to tackle a pressing global issue: water quality degradation.
The study, published in ‘Discover Civil Engineering’ (which translates to ‘Civil Engineering Discoveries’), is a beacon of hope for industries grappling with water quality challenges, particularly in the energy sector where water is a critical resource. Traditional methods of water quality assessment are often expensive, labor-intensive, and complex. Das and his team have turned to machine learning methodologies to revolutionize this process.
“Traditional methods are not only time-consuming but also costly,” Das explains. “By integrating machine learning techniques, we can provide a more efficient, reliable, and cost-effective solution for water quality monitoring.”
The research focuses on the Mahanadi River Basin, where water quality parameters such as Total Kjeldahl Nitrogen (TKN) and coliform levels have been found to exceed acceptable limits. The team employed a combination of machine learning algorithms, including Genetic Algorithm Particle Swarm Optimization-based Water Quality Index (GAPSO-WQI), Firefly Algorithm (FA), and Algorithm of Weeds (AW), to evaluate the water quality for drinking purposes. The geographic variability of point properties was interpolated using geospatial techniques like Inverted Distance Weighted (IDW) and visualized using ArcGIS 10.5 software.
The findings are compelling. The GAPSO-WQI method revealed that water quality in the region ranges from low to high, with 50% of the surveyed sites indicating that domestic water supply degradation is a primary cause of contamination. The AW framework further categorized the water quality, showing that 50% of the sites fall under low-medium water quality, while 18.75% are in the poor category. Despite these challenges, the study found that most samples were categorized as fair for human consumption.
The research also delved into the suitability of surface water for irrigation, utilizing models such as Cat Boost (Cat B), AdaBoost (AB), and Gradient Boosting (GB). The results were promising, with the surface water deemed appropriate for irrigation based on various irrigation indices. The study suggests that merging modeling approaches from Cat B, GB, and AB could achieve consistent and favorable outcomes in irrigation forecasting, potentially cutting down on analysis time and expenses.
“This research is not just about monitoring water quality; it’s about providing actionable insights that can drive decision-making and policy formulation,” Das notes. “The implications for the energy sector are significant, as water is a critical resource for many energy production processes.”
The study’s innovative use of machine learning and AI techniques offers a glimpse into the future of water quality assessment. By leveraging these technologies, industries can expect more efficient, reliable, and cost-effective solutions for monitoring and managing water resources. As the world grapples with increasing water scarcity and quality issues, this research provides a roadmap for harnessing the power of AI and machine learning to address these challenges head-on.
In an era where data is king, Das’s work underscores the transformative potential of machine learning and AI in the field of water quality assessment. As industries continue to seek sustainable and efficient solutions, this research offers a beacon of hope, paving the way for a future where technology and innovation converge to tackle some of the world’s most pressing water quality challenges.