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@article{176097,
        author = {Chaitanya Jadhav and Sudarshan Takate and Mann Bisht and Dr. D.B. Salunke},
        title = {ELUDE: Enhanced Landslide Understanding and Detection Engine Using IoT and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7871-7875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176097},
        abstract = {Landslides remain a persistent and deadly threat in regions with high rainfall and unstable terrain. To address the challenge of early detection in such areas, this paper presents ELUDE (Enhanced Landslide Understanding and Detection Engine), a low-cost, Wi-Fi-enabled landslide prediction and alert system. The system is built on the ESP32 Wroom microcontroller, supported by dual analog-to-digital converters for seamless integration of multiple analog sensors. These include modules for rainfall intensity, temperature and humidity, and ground vibration, which provide a comprehensive overview of environmental conditions. Collected data is transmitted wirelessly to a remote server for real-time analysis using a machine learning-based anomaly detection model. The proposed system emphasizes modularity, affordability, and ease of deployment in remote or inaccessible terrains. Key contributions include: (1) a unified hardware-software architecture for continuous monitoring, (2) real-time data acquisition and transmission using lightweight IoT protocols, and (3) predictive alert generation based on terrain behaviour patterns. Field-level testing demonstrates the system’s accuracy and responsiveness, highlighting its potential for integration into regional disaster preparedness frameworks.},
        keywords = {ESP32 Wroom, Analog-to-Digital Converter, Machine Learning, Sensor Network, Vibration Sensor, Rainfall Detection, Wi-Fi Communication.},
        month = {May},
        }