AI for Water Sustainability
India faces a lot of water problems like non-revenue water, groundwater depletion, and water pollution. AI can significantly improve water monitoring, conservation, and distribution with tools like predictive analytics, smart sensors, and decision support systems. However, there will be barriers such as poor data quality, limited infrastructure, and digital literacy gaps.
STEM RESEARCHENVIRONMENTAL STUDIESAI
Hashiny Priya Sivakumar Revathi
8/10/20253 min read
Abstract:
Non-Revenue Water (NRW) refers to water that is produced but lost before it reaches customers, making it unbillable. According to estimates, NRW cost India $5 billion in 2019 and the global economy $39 billion.
India also faces other water challenges like groundwater depletion, river pollution and growing demand.
The root causes include ineffective planning, malfunctioning systems, and ineffective utility operations.
Although national policies have attempted to integrate technology, they have not been successful. However, AI offers a powerful tool the government can use to help solve the problems related to these issues and reduce NRW.
Moreover, India has a rare chance to capitalize on the momentum being created globally and apply AI to revolutionize its water and sanitation industries.
Introduction:
India has about 16% of the world’s population, but accounts for about 4% of the world’s freshwater.
Contributing factors include limited regulation, insufficient public investment, and ineffective governance structures. All these led to limited access to clean and safe water for multiple generations.
These challenges have escalated into regional disputes over river access within the country. It intensified regional tensions with Pakistan over the Sutlej River and China over the Brahmaputra River.
Moreover, India’s groundwater usage is estimated to be roughly one-fourth of the global usage, surpassing China and the United States. In some regions, the water table has dropped by 4 meters.
Additionally, more than half of the rivers in India are highly polluted at levels considered unsafe by modern standards.
In this context, AI offers a promising role to monitor, analyze, and address a wide range of issues. AI can help regulate water-related issues in India.
However, challenges such as digital literacy gaps and infrastructure limitations may hinder the implementation.
This paper explores how AI can be used to fight water pollution, reduce NRW, and groundwater depletion. While also exploring strategies to overcome key barriers to its adoption.
Literature Review:
1. AI is transforming groundwater management through predictive modeling, real-time monitoring, data integration, decision support, and optimization. Predictive modeling uses historical data to forecast groundwater levels, enabling proactive planning and reducing overextraction risks. Real-time monitoring combines IoT sensors and AI to detect quality issues instantly, supporting quick responses to contamination. Data integration merges satellite, field, and climate data to improve flow modeling accuracy and resource assessment. Decision support systems (DSS) use scenario-based models to guide efficient allocation and conservation strategies. Optimization algorithms can be used to schedule groundwater extraction, minimizing both energy use and environmental impact.
However, challenges include data scarcity and poor data quality, due to limited monitoring infrastructure and inconsistent formats. Additionally, there is a lack of cross-disciplinary expertise in both hydrogeology and AI, which limits effective implementation.
AI can help monitor and analyze water pollution through models like ANN, DNN, LSTM, and Random Forest, which excel at handling nonlinear, non-stationary water quality data from rivers.
Integration of IoT with AI enables continuous data collection, improving real-time detection and prediction of pollution events. AI can help not only detect pollution but also suggest data-driven pollution control methods.
Yet, some challenges include identified barriers, including data quality issues, lack of standardized datasets, and the need for more integrated systems. Emerging opportunities include deeper AI-IoT fusion and improved real‑time control systems.
Artificial Intelligence (AI) plays a crucial role in reducing Non-Revenue Water (NRW). AI helps water utilities detect and prevent these losses more efficiently through leak detection and Prediction analyzes sensor and acoustic data to identify hidden leaks early and predict future leak locations. Smart Meter Analysis detects unusual consumption patterns, helping catch theft or faulty meters quickly. Pressure Management systems dynamically control water pressure to reduce stress on pipes and prevent bursts. Demand Forecasting predicts water demand, helping to balance supply and avoid over-pressurization that can cause leaks. Decision Support Tools integrates data from sensors, climate, and infrastructure to guide maintenance and investment decisions.
By enabling faster detection, real-time monitoring, and predictive maintenance, AI significantly reduces water loss, saves costs, and ensures more sustainable water distribution, which helps reduce NRW.
Case Study: Around mid-2024, as a part of the smart cities mission, the city of Panaji in Goa, India conducted a project to tackle Non-Revenue Water using advanced IoT and AI technologies. Flow meters and pressure transmitters were deployed on main distribution lines. Flow meters and pressure transmitters were installed, along with integrated systems for real-time monitoring and automated usage alerts. Around 3,094 smart water meters were deployed out of approximately 7,200 households. NRW dropped dramatically from 38% to 15%. This enabled billing, early detection of leaks, meter tampering and faulty meters. This success was made possible by the integration of IoT and AI analytics, which identified anomalies and flagged issues quickly.
Conclusion: In conclusion, India can overcome its water problems like Non-Revenue water, water pollution, and groundwater depletion with the help of AI. However successfully implementing it depends on challenges like data limitations, infrastructure gaps, and digital literacy. With strategic planning and execution AI can play a pivotal role in addressing India’s water challenges.
Sources:
https://siwi.org/latest/water-crisis-india-everything-need-know/
https://ijsra.net/sites/default/files/IJSRA-2024-0105.pdf
https://pubmed.ncbi.nlm.nih.gov/40112582/
https://siwi.org/latest/water-crisis-india-everything-need-know/