Using tech to predict the next natural disaster, strengthen early warnings

Advanced algorithms and satellite networks are transforming real-time climate forecasting to save lives around the world

The Nepal-Tibet glacier break has raised questions about using artificial intelligence (AI) to predict natural disasters. 

 

Institutions and researchers around the world are using algorithms to predict disasters. Indian organisations have developed disaster-warning systems that rely on AI and information from satellites.

 

AI, machine learning (ML) and geographic information systems (GIS) help predict and manage disasters by analysing vast amounts of data from satellites, weather stations, sensors, radar systems, and historical records. These technologies identify complex patterns and relationships often difficult to detect through conventional methods, significantly improving the speed, accuracy and reliability of early-warning systems. 

 

AI models help detect the early signs of extreme weather events such as floods, cyclones, hurricanes, wildfires and severe storms, allowing the authorities to identify vulnerable regions and take preventive measures. Although AI cannot predict when an earthquake will occur, it can assess seismic risks and estimate the probability.

 

A significant application of AI and ML is in flood forecasting. Traditional hydrological models rely on rainfall measurements, river discharge data and terrain information to estimate water levels and predict flooding. While effective, these models often face challenges in processing large volumes of real-time information from multiple sources. AI enhances these capabilities by integrating diverse datasets, including satellite imagery, weather radar observations, meteorological forecasts, sensor networks, urban drainage information, and even publicly available data from social media platforms. 

 

ML algorithms analyse this information in real time to predict the timing, location, and severity of floods, providing communities and disaster management agencies valuable lead time for preparedness and response measures.

 

Google’s flood forecasting system has been deployed in India and Bangladesh. Using deep learning techniques and large volumes of historical and real-time hydrological and meteorological data, the system can generate flood forecasts several days in advance. The warnings are delivered directly to users through smartphones in local languages, enabling people to take timely action.

 

GIS-based models can determine whether intense rainfall and flooding are likely to trigger landslides or mudslides in vulnerable areas and can predict the movement of debris flows in high-risk regions. 

 

India has been incorporating AI, ML and big data for weather forecasting and disaster management. The National Centre for Medium Range Weather Forecasting, which works under the Ministry of Earth Sciences (MoES), has integrated AI- and ML-based tools with advanced operational systems. AI-generated products have also been incorporated into India’s indigenous GIS-based Multi-Hazard Early Warning Decision Support System to strengthen disaster risk reduction and emergency response.

 

The MoES has three AI-based global weather models that assist forecasters in predicting severe weather events and improving overall forecast accuracy. In addition, scientists have developed indigenous AI and ML applications for  forecasting extreme weather events.

 

India’s meteorological infrastructure includes 50 Doppler weather radars along with 1,008 automatic weather stations and 6,885 rain gauge stations that support rainfall monitoring at district and block levels.

 

The MoES has established research collaborations with the Indian Institute of Technology Kharagpur, Indian Institute of Information Technology Vadodara, IIIT Allahabad, Ashoka University and Bharat Electronics Limited, strengthening the country’s capabilities in AI-based weather forecasting, climate modelling and disaster risk management. Technology-based warning systems will become more effective with hyperlocal information and focused intelligence gathering. Investments in early warning systems are likely to accelerate around the world.

 

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The writer is an economic analyst and author