Digital transformation of early warning system for floods in India

India is undergoing a rapid transformation. Digital technology is being used to develop early warning systems for floods. How did this happen, and who is supporting this work?

India is one of the most flood-prone countries due to its diverse geography and large rivers such as the Ganges, Brahmaputra, and Indus. Floods are the most common natural disaster in the country, a situation that has worsened due to climate change. If there were 8 episodes in the 20th century, in the 21st century, the figure has tripled. The last 5 years have been particularly striking, covering 50 per cent of the floods that have occurred in this century.

As the National Disaster Management Authority (NDMA), headed by the Prime Minister of India, points out, more than 40 million hectares of the country’s total geographic area (329 million hectares) are flood-prone. On average, 7.5 million hectares are affected annually, and beyond the loss of human lives, damage to crops, housing, and public services amounts to an annual average of almost 2,000 crore rupees (€21.66 million).

While India has made significant progress in water management digitalisation in recent years—adopting innovative technologies such as the Internet of Things (IoT), Big Data, and Artificial Intelligence—the implementation of Early Warning Systems (EWS) must be expanded to mitigate the impact of floods by enabling faster and more effective responses.

Digital technologies for flood prediction and warning

India’s EWS is a combination of traditional and digital systems that collect data from hydrometeorological stations, river and reservoir sensors, and satellite imagery. The Central Water Commission (CWC) and the India Meteorological Department (IMD) are the primary entities responsible for issuing flood alerts.

Advances in digital transformation have enabled the integration of innovative technologies into EWS, improving both the accuracy and speed of flood alerts,” Tushar Tyagi, Senior Manager at Xylem Vue India, said. For the expert, three technologies are key in Early Warning Systems:

Big Data and Machine Learning—Processing massive datasets (Big Data) and machine learning algorithms are fundamental to flood predictive models, as they enable the analysis of weather patterns, water levels, and topographic conditions.
Internet of Things (IoT) and Remote Sensors – Deploying IoT sensors in rivers, reservoirs, and high-risk areas enables real-time data transmission on water levels, flow velocity, and rainfall. These sensors also support early detection and instant alert transmission.
Geographic Information Systems (GIS) and Remote Sensing—According to the Xylem Vue expert, satellite imagery from sources such as NASA, the Indian Space Research Organisation (ISRO), and other agencies “is fundamental to information about the spread of floods. The use of GIS allows the creation of risk maps in real time, facilitating decision-making.”

However, a number of challenges set the agenda for implementing Early Warning Systems in India. Ankur Chaurasia, Senior Solution Architect at Xylem Vue India, points out key barriers to EWS expansion in the country: “The lack of monitoring stations in some regions, as well as their limited internet connectivity, makes it difficult to collect accurate data and transmit alerts. Additionally, greater public-private collaboration and increased awareness and education about these warning systems are needed to ensure an effective response.”

Strategies for a more effective EWS

Therefore, improving the Early Warning System involves implementing strategies reinforcing India’s resilience to extreme monsoon-related events. In this regard, Tushar Tyagi outlines the following methods to strengthen India’s EWS:

Expansion of monitoring networks: According to the Xylem Vue expert, increasing investment in instruments such as IoT sensors and meteorological stations is key to improving forecast accuracy.
Improvement of data infrastructure: Cloud-based technologies play a crucial role in enhancing the efficiency of data collection and distribution.
Integration of artificial intelligence: Tyagi emphasises that one of the most critical strategies for improving efficiency “lies in applying more advanced predictive models that enable more accurate event forecasting.”
Community participation and education: Finally, the experts stress the importance of implementing training and awareness programs to ensure communities understand and effectively use alerts.

Digitalisation has undoubtedly significantly improved India’s ability to predict and manage floods, but challenges remain. To enhance its EWS, India must continue investing in technology, strengthening stakeholder collaboration, and raising public awareness. With the right strategies, the country could significantly reduce the impact of floods, protecting lives and the economy.