MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202611094276 A) filed by Dr. Noureen Ali; Dr. Mohini Mittal; Dr. Shaiqa Nasreen; Dr. Romana Yousuf; Dr. Hilal Ahmad Sheikh; Dr. Raj Kumar Paul; Er. Ankur Sharma; Dr. Rishika Mehta; Mr. Sanjay Kumar Nayak; Dr. Mani Goyal; Dr. Insha Ishtiyaq; and Dr. Ismat S Masoodi on August 04, 2026, for A System & Method For Smart & Early Landslide Prediction Based Upon Internet Of Things And Machine Learning.
Inventors include Dr. Noureen Ali; Dr. Mohini Mittal; Dr. Shaiqa Nasreen; Dr. Romana Yousuf; Dr. Hilal Ahmad Sheikh; Dr. Raj Kumar Paul; Er. Ankur Sharma; Dr. Rishika Mehta; Mr. Sanjay Kumar Nayak; Dr. Mani Goyal; Dr. Insha Ishtiyaq; and Dr. Ismat S Masoodi.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: This article details a state-of-the-art approach to landslide prediction that makes strategic use of wireless sensor networks (WSNs), global positioning system (GPS) devices, cellular networks, and the Internet of Things (IoT). The recently developed technology is capable of foreseeing potential hazards, obstructions, and environmental factors because it is comprised of sensors connected to the Internet of Things and wireless sensor networks. In addition, GPS and GSM modules will be utilised by the system to precisely locate the accident and relay that information to the proper authorities. The current implementation of the invention is an effective tool that provides the user with real-time alerts and notifications. The sensor feeds information into the system, which is then analysed by the microcontroller to determine the next steps to take. In addition to the methods already mentioned, satellite images are also used to compile data. The system provides a number of advantages, including a high degree of intelligence, the ability to accurately predict and forecast outcomes, a wealth of monitoring tools, and online, real-time monitoring. A schematic of the components of the invention is shown in
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