In the heart of Maharashtra’s rural landscapes, where the Jal Jeevan Mission is transforming lives by bringing piped water to millions, a quiet revolution in water quality management is underway. Researchers S. R. Lolapod and S. J. Mane have developed an artificial intelligence-driven tool that could redefine how chlorine—vital yet volatile—is managed in water distribution networks. Their study, published in *Nature Environment and Pollution Technology* (translated from *Processes in Nature and Environmental Pollution*), demonstrates how artificial neural networks (ANNs) can predict residual chlorine levels with remarkable accuracy, offering a pathway to safer, more efficient rural water systems.
The challenge is familiar to water utilities worldwide: chlorine, the backbone of disinfection, degrades unpredictably as water travels through miles of pipelines. Factors like pH, temperature, and distance from treatment plants all influence how much chlorine remains by the time it reaches a household tap. Traditional methods rely on fixed dosing or reactive adjustments—approaches that often lead to either over-chlorination (wasting resources and creating harmful byproducts) or under-dosing (risking contamination). But in a study conducted across the Rui and Shingave Water Supply Scheme, Lolapod and Mane show that AI can change the game.
Using 250 real-world samples collected during a winter period in December 2024, the team trained a Multilayer Perceptron (MLP) model using NeuroSolutions v6 software. The model achieved a correlation coefficient of 0.902 on test data, with mean absolute error just 7.18 and mean squared error 114.25—metrics that indicate strong predictive performance. “This isn’t just about numbers,” Lolapod noted. “It’s about turning data into action. With this model, we can anticipate chlorine loss before it happens, not just react to it.”
For energy and utilities stakeholders, the implications are significant. Accurate, real-time prediction of chlorine decay means water utilities can optimize dosing, reducing chemical usage and energy costs tied to pumping and treatment. Over-chlorination not only wastes energy in dosing systems but can also accelerate corrosion in metal pipes—a hidden cost often overlooked in rural networks. By fine-tuning chlorine levels, utilities could cut operational expenses while improving public health outcomes.
Looking ahead, the integration of such ANN models with SCADA or IoT platforms could enable dynamic, automated control of water treatment processes. Imagine a rural water system where sensors feed real-time data into an AI model, which then adjusts chlorine dosing automatically—minimizing waste, maximizing safety, and reducing the need for manual intervention. This is not science fiction; it’s a practical evolution, especially in regions like Maharashtra, where infrastructure upgrades are still underway.
As rural water systems modernize under missions like Jal Jeevan, tools like the one developed by Lolapod and Mane offer more than just technical innovation—they provide a foundation for smarter, more resilient water management. In a sector where every drop counts and every rupee of operational cost matters, the shift from guesswork to prediction could be the difference between a system that merely functions and one that thrives.