Artificial intelligence as a tool for upholding humanrights in disaster management: A case study of wayanad landslides

            Dr n l  sajikumar ,Associate Professor, Government Law College, Kerala, India

                    Dr Sanju.V.K Associate Professor, GovernmentLaw College, Kerala, India

                   saina S Asok, LL.M, Arbitration, Jindal Global Law School, Haryana, India

The study is based on the report and assessment of materials collected from various sources with respect to the landslide disaster in Waynad ,Kerala, India  that occurred in 2024,wherein there were loss of human lives as well as property that resulted in huge commercial loss endangering human rights. This incident led the researchers to explore the application of artificial intelligence in detecting the possibility of such disasters and protecting the human rights of the people.

Approximately 24% of the Earth's land features uneven surfaces, which are home to around 12% of the global population. In areas characterized by such irregular landscapes, the likelihood of soil and rock mass movement, referred to as landslides, is significantly increased due to the direct effect of gravity. In this scenario the researchers intend to explore the application of Artificial Intelligence (AI) in enhancing human rights protection during disaster management, focusing on the Wayanad landslides in Kerala,, the Gods own country in India. . In India, areas like Wayanad in Kerala are prone to landslides due to their unique topography and climatic conditions. A Case Study of Wayanad Landslides natural disasters pose significant threats to human rights, particularly in vulnerable regions of the world The catastrophic landslides in Wayanad in 2024   underscored the necessity for innovative disaster management approaches that leverage technology to protect lives and uphold human rights. Artificial Intelligence (AI) has emerged as a pivotal tool in disaster management, offering predictive analytics, resource optimization, and effective response strategies. This article explores the potential of AI in enhancing disaster management practices, specifically focusing on the case of the Wayanad landslides and its implications for human rights .Disasters can severely infringe on human rights, including the right to life, health, and a safe environment. The United Nations Office for Disaster Risk Reduction emphasizes the need to integrate human rights considerations into disaster risk reduction and management strategies. In the case of Wayanad, the landslides resulted in widespread destruction of homes, displacement of communities, and loss of life, highlighting the urgent need for effective disaster preparedness and response mechanisms. In this scenario the researchers intent to introspect the need for leveraging Artificial intelligence technology to forecast landslides  for an early warning systems employing AI algorithms can significantly improve response times and enable communities to evacuate before disasters strike, thereby protecting lives and minimizing human rights violations.

Keywords-Artificial intelligence-Landslides-Waynad-Disaster management-Human Rights

How to cite: v k, D. S., n l, D. S., and s asok, S.: Artificial Intelligence as  a tool for upholding human rights in disaster management: A Case Study of Wayanad landslides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12575, https://doi.org/10.5194/egusphere-egu25-12575, 2025.

This presentation shows the role of weather informatics in addressing critical societal challenges by integrating meteorological science with advanced data analytics and user-centric geospatial visualization tools. Key innovations include high-resolution weather forecasting, radar and satellite data processing, and real-time sensor network integration with robust visualization platforms designed to meet operational demands.

At the core of this approach is the Weather Image Information System (WIIS), a weather informatics platform created to process, analyse, and share extensive volumes of meteorological image data. By integrating diverse data sources—including satellite systems, ground-based radars, and real-time sensor networks—WIIS generates high-resolution imagery, enabling precise monitoring of weather patterns. This system offers interactive geospatial maps, dynamic weather animations, and customizable overlays, facilitating detailed analysis of atmospheric phenomena and enhancing real-time situational awareness.

WIIS also provides advanced decision-support capabilities, allowing users to set customizable alert thresholds for severe weather events. These features enable proactive disaster preparedness, safeguard operational continuity, and support critical infrastructure in the energy and transport sectors.

How to cite: Paulitsch, H. and Tani, S.: Advancing Weather Informatics for Meteorological Data Management and Decision Support Systems in the Energy and Transport Sectors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18359, https://doi.org/10.5194/egusphere-egu25-18359, 2025.

A fully automated IoT measurement network was installed in these 3 cities' urban areas (as well as in the surrounding rural areas), measuring air temperature and precipitation at typically ≥50 different locations selected according to scientific and social criteria, covering all local climate zones and points of interest, in places where the (much more costly) official WMO-standard stations can not operate due to technical restrictions. Where available, data from existing measurement systems were be integrated into the processing chain. The real-time IoT monitoring system was calibrated with local WMO-standard quality-controlled measurements,  utilised satellite data and micro-scale models developed by meteoblue,  to generate special city climate maps (e.g., heat maps which detect and visualise the urban heat island effect at the spatial resolution of 10 m, cold air flow maps, or precipitation risk maps). The real-time monitoring system and resulting maps were integrated into existing city management platforms.

Applications include using these data with a surface energy balance model to calculate possible options for climate change adaptation measures (e.g., roof greening, irrigation, de-sealing of surfaces) for urban hot-spots, to select the best adaptation strategies for parts of the city. Additionally, the effectiveness of  climate change adaptation measures in the process of being implemented can be tracked, so the economic effectiveness of the measures can be assessed, by comparing with other locations where no adjustment took place.

The combination of IoT network and Microscale modelling provides better results of modelling, cold air flow tracking and  measuring adaptation effectiveness at a significantly lower cost of implementation and operation than alternative methods.

How to cite: Denzer, H. W., Grutbrod, K., Bader, N., and Schloegl, S.: City Climate Monitoring System for Zürich, Basel and Tallinn, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17964, https://doi.org/10.5194/egusphere-egu25-17964, 2025.

The increasing use of Unmanned Aerial Vehicles (UAVs) across various sectors underscores the necessity for thorough testing under diverse meteorological conditions to ensure operational safety and reliability. The IFIRE project, led by AIRlabs Austria in collaboration with Pegasus Research & Development GmbH, Graz University of Technology, and FH JOANNEUM, addresses this important challenge by focusing on assessing UAV performance in adverse weather conditions, particularly in relation to icing.

The primary objective of the project is to enhance aviation safety and efficiency by integrating advanced weather diagnostic and forecasting capabilities into UAV operations. A comprehensive methodology is proposed, which includes developing a sophisticated weather forecast model, machine learning approaches, conducting flight tests to collect critical data, and evaluating natural icing conditions at the Steinalpl test site in Austria. IFIRE aims to establish new safety and reliability benchmarks for UAVs by creating a state-of-the-art flight-testing area specifically designed for natural icing conditions. The multidisciplinary consortium brings together technical, regulatory, environmental, and operational expertise to address the challenges of UAV testing in icy environments. Information on the initial phase of the project and future steps will be presented.

How to cite: Tani, S., Paultisch, H., Deutsch, R., Fallast, A., Neubauer, T., Kucera, M., and Puffing, R.: Advanced Diagnostic and Forecast System of Icing Conditions for UAV Operational Safety, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16460, https://doi.org/10.5194/egusphere-egu25-16460, 2025.