How AI and IoT are Reshaping Landslide Management

Landslides are one of the geological disasters with wide distribution, high impact, and serious global damage. Between 2015 and 2020, 3782 landslides occurred in India, affecting approximately 426 million people, constituting 30% of India's population(Fig 1).

In India, landslides mostly occur in the monsoon season, especially in the? Himalayas and Western Ghats which are highly susceptible to mass movements due to hilly topography and heavy rainfall. Landslide Atlas of India highlights various causes for these landslides. Some of the most devastating incidents include the landslides of Sikkim in 2011, Kedarnath in 2013, Assam and Manipur in 2022, the Raigad landslides in 2023, and the recent Wayanad landslide in Kerala in 2024 which received 6% of its annual rainfall in just a few hours (Fig 2).?

Critical Factors that Contribute to the Disaster

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.?

While development is both evident and important, finding a balance between progress and sustainability is key to building a better future.

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 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.?

As climate change intensifies, we can expect an increase in the frequency and severity of extreme weather patterns and rising rainfall shortly. More hyperlocal weather measurement networks, improvement of data accuracy in weather model forecasting, and high-resolution geostationary satellites are urgently needed to address the growing threats of natural disasters like landslides. 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.