MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202611089021 A) filed by Dr. Chintalapati Neelima Rani; Dr. Navya Kakarlapudi; Dr. Vijay Kumar Gumasa; Annu Sharma; Mr. K. C. Mohanraj; Pavithra G; Dr. S. Arulmozhi; Dr. L. Malathi; Dr. Blessy Queen Mary M; Urlam Pranita; Megha Chaitanya Singru; and Dilip Mishra on July 21, 2026, for Machine Learning-Driven Iot Platform For Early Warning Of El Niño Events And Assessment Of Global Warming Impacts.
Inventors include Dr. Chintalapati Neelima Rani; Dr. Navya Kakarlapudi; Dr. Vijay Kumar Gumasa; Annu Sharma; Mr. K. C. Mohanraj; Pavithra G; Dr. S. Arulmozhi; Dr. L. Malathi; Dr. Blessy Queen Mary M; Urlam Pranita; Megha Chaitanya Singru; and Dilip Mishra.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: The current invention reveals a Machine Learning-Driven IoT Platform for the Early Warning of El Niño Events and Evaluation of Global Warming Effects. The invention amalgamates Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Geographic Information Systems (GIS), Remote Sensing, Big Data Analytics, and Cloud Computing to facilitate real-time climate monitoring, early warning, and sophisticated decision support. The platform gathers environmental data from IoT-enabled weather stations, ocean buoys, satellite observations, meteorological stations, hydrological monitoring systems, atmospheric sensors, and climate databases. The gathered data encompass sea surface temperature, atmospheric pressure, precipitation, humidity, wind velocity, ocean currents, sea level, carbon dioxide concentration, air temperature, and more climatic variables. The collected data are subjected to preprocessing, normalisation, and analysis utilising sophisticated machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression, Artificial Neural Networks (ANN), and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to forecast the occurrence, intensity, and duration of El Niño events. The system assesses long-term global warming effects by examining climatic trends, greenhouse gas emissions, temperature anomalies, sea-level rise, and extreme weather phenomena. A GIS-based visualisation module delineates climate risk zones, while an advanced risk assessment module produces early warnings for droughts, floods, heatwaves, and other climate-related threats. The decision-support module offers guidance for disaster preparedness, agricultural planning, water resource management, and climate adaption. A secure cloud-based dashboard facilitates real-time visualisation of climate data, predictive analytics, danger maps, and AI-generated suggestions. The suggested invention boosts forecasting precision, fortifies climate resilience, improves catastrophe preparedness, and promotes sustainable environmental management via intelligent, data-driven climate monioring and early warning systems.
Disclaimer: Curated by HT Syndication.
