Delhi Jal Board and IIT Kanpur to integrate AI use into water management (Representative Image)
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Delhi Jal Board, IIT-Kanpur ink pact to bring AI into water management

Under the partnership AI-driven solutions for reducing non-revenue water, predictive maintenance of infrastructure, & others will be implemented

Anup Verma

In a move to make Delhi’s water systems more efficient, transparent, and technology-driven, the Delhi Jal Board on Monday signed a Memorandum of Understanding (MoU) with the Airawat Research Foundation (ARF) — India’s National AI Centre of Excellence in Sustainable Cities, established under the Government of India at IIT Kanpur.

The MoU, signed in the presence of Water Minister Parvesh Sahib Singh Verma, senior DJB officials and IIT Kanpur representatives, is non-financial and non-binding, aimed at collaboration in research, data integration, and deployment of advanced AI technologies to modernise water and wastewater management in Delhi.

Under the partnership, DJB and ARF will design and implement AI-driven solutions for reducing non-revenue water, predictive maintenance of infrastructure, and real-time monitoring of water and sewage treatment plants.

The project will also develop AI-based grievance redressal and revenue management systems to ensure faster complaint resolution, improve billing accuracy, and enhance customer satisfaction. It will support the Clean Yamuna Mission through AI-assisted monitoring of wastewater discharge and pollution sources.

The scope further includes AI-assisted groundwater monitoring and recharge planning, using borewell data, sensors, and satellite imagery for real-time tracking and sustainable aquifer management.

Water Minister Parvesh Verma said the partnership reflects the government’s commitment to Digital India, adding, “These tools will help detect problems before they happen, improve efficiency, and rebuild citizens’ trust in public services.”

Key focus areas under the MoU include

AI-enabled public grievance redressal for faster, fairer complaint resolution

AI-driven revenue management system to improve billing accuracy and reduce losses

Digital Twin models for water and sewage treatment plants to enable predictive maintenance

AI-based pollution monitoring and predictive planning for the Clean Yamuna Mission

Groundwater Digital Twin for real-time monitoring, forecasting, and recharge planning

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