International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 4
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A Cloud-Based Hybrid CNN-LSTM System for Real-Time Landslide Prediction Using Geospatial Intelligence

Author(s) Mrs Swati Vilas Chiplunkar, Mrs. Monisha Linkesh, Dr. Namdeo Badhe, Dr. Anil Vasoya
Country India
Abstract Landslides pose a significant threat to life, infrastructure, and economic stability, especially in rainfall-prone and geologically sensitive regions. In this study, we propose a cloud-based landslide prediction system that integrates geospatial data processing with a hybrid deep learning approach. The system begins with the creation of a landslide susceptibility dataset using Quantum GIS (QGIS), incorporating both static (e.g., slope, elevation, land use) and dynamic (e.g., rainfall) environmental features. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model is developed to capture spatial patterns and temporal dependencies, offering improved prediction accuracy over conventional models. The system is deployed as a web-based application using HTML, JavaScript, and MySQL, with the backend hosted on Google Cloud Platform (GCP) for scalability and real-time performance. Testing with hybrid pretrained models further enhances prediction reliability. This research demonstrates the effectiveness of combining geospatial tools and deep learning for early warning and disaster mitigation in landslide-prone areas. The proposed system has strong potential for integration into national disaster management frameworks to support timely and data-driven decision-making.
Keywords Landslide Prediction, Hybrid CNN-LSTM Model,Geospatial Data Analysis,real-Time Early Warning System,Cloud Computing (Google Cloud Platform)
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-18
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E-ISSN 2582-2160
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