India’s landslide catastrophe: How AI and IoT can save lives
Introduction
Landslides in India are among the most devastating natural disasters, causing significant global damage and leaving a trail of destruction for the people and areas impacted. Between 2015 and 2020, India faced a staggering 3,782 landslides, affecting approximately 426 million people, constituting 30% of India’s population (Fig 1). These events underscore the urgent need for advanced technological interventions in landslide-prone areas in India.
In India, landslides mostly occur during the monsoon season, especially in the Himalayas and Western Ghats, which are highly susceptible to mass movements due to their hilly topography and heavy rainfall in India. In 2011, the peaceful landscapes of Sikkim were violently altered, and in 2013, chaos erupted in Kedarnath, claiming countless lives. Recently, in 2022, the landslides in Assam and Manipur left communities in ruins, while the 2023 Raigad tragedy wiped out entire villages. And just this year, in 2024, Wayanad in Kerala was struck by a deluge that delivered 6% of its annual rainfall in mere hours (Fig 2). These are not just statistics — they are stories of unimaginable loss and suffering, of communities forever changed by India landslide disasters.
Critical Factors that Contribute to the Disaster
These disasters are not just acts of nature or mere coincidence but are often the result of causes that are manageable. The Landslide Atlas of India highlights several critical causes behind these devastating events, including deforestation, unplanned construction, mining activities, and the destabilization of slopes. Additionally, climate change-induced extreme weather patterns further exacerbate the risk in landslide-prone areas in India.
For natural disasters like landslides, proactive disaster management practices are vital. Implementing measures such as effective slope management in advance is required to ensure sustainable and resilient growth. Deforestation, mining, construction, and other human activities can destabilize slopes by removing vegetation, altering drainage patterns, and adding weight or vibration to the land. In addition to changes in agricultural practices and deforestation, changing climate and associated extreme precipitation exacerbate the condition and increase the risk of landslides in India.
The growing frequency of such events, exacerbated by climate change and human activities, underscores the urgent need for advanced technological interventions.
Enhancing Landslide Prediction, Detection, and Response with AI and IoT
Even with forecasting from meteorological agencies, leveraging satellite and model simulations, a landslide-vulnerable region can still face challenges. While the long-running challenges in weather forecasting are accuracy and spatial resolution, a key challenge with current satellites is their limited temporal resolution (day-to-week revisit time). This can adversely impact monitoring high-risk zones, probability of risk estimation, and decision-driven solutions in landslide-prone areas in India.
In this context, the inclusion of AI and technology in disaster management can help forecast potential disasters with better accuracy and resolution. AI can help predict, prepare for, respond to, and recover from landslides more effectively, ultimately saving lives and reducing suffering. Meanwhile, technology can improve landslide detection and predictions at sensitive and vulnerable zones by using machine learning algorithms in combination with hyper-locally installed IoT-based sensors to get granular ground-based information. Sensors such as rainfall, vibration, soil moisture, temperature, humidity, and water flow collect real-time data. This combination facilitates the integration of satellite data with sensor and weather model predictions, which helps to gain comprehensive environmental insights towards unexpected environmental changes.
By leveraging cutting-edge sensors and advanced data analysis techniques, Aurassure can significantly enhance landslide forecasting capabilities. Aurassure’s IoT sensors capture critical rainfall in India data, which, when combined with historical and current climate data, can predict potential landslide triggers with greater precision. This advanced forecasting capability enables timely alerts and preventive actions, helping to mitigate the impact of landslides in India and protect vulnerable communities from the devastating effects of extreme rainfall events. Additionally, by integrating this data with weather forecasts and geological information, Aurassure can provide highly accurate and localized predictions, enabling authorities to take proactive measures, evacuate vulnerable populations, and ultimately save lives and communities before disaster strikes.
The synergy between satellite data, IoT sensors, and AI-driven analytics offers a robust framework for comprehensive environmental monitoring. This integrated approach enables more accurate forecasting, early detection of potential landslides, and effective response strategies, ultimately saving lives and reducing the impact of these natural disasters.
Conclusion
As climate change intensifies, we can expect an increase in the frequency and severity of extreme weather patterns and rising rainfall in India. The time for action is now. We need more hyperlocal weather measurement networks to capture the nuances of these violent storms, sharper accuracy in weather models to foresee the dangers ahead, and high-resolution geostationary satellites that can keep monitoring over our most at-risk areas continuously. Finally, an integrated and intelligent approach, combined with tech-driven information dissemination for accurate forecasting, early warning, and timely action, can contribute to a data-driven, actionable, and sustainable future.
