Next-Generation Landslide Prediction: Convergence of IoT Sensors, Remote Sensing, Edge AI, and Quantum Computing

Abstract

Landslides are among the most destructive natural hazards, causing extensive damage to infrastructure, ecosystems, and human lives worldwide. Conventional monitoring and prediction methods often face challenges such as limited scalability, delays in data acquisition, and inadequate predictive accuracy. This review presents a next-generation framework for landslide prediction that integrates Internet of Things (IoT) sensor networks, remote sensing, edge artificial intelligence (AI), and quantum computing. IoT devices deployed in high-risk areas continuously capture critical geotechnical and environmental parameters, including soil moisture, pore pressure, slope inclination, and ground vibrations. When IoT data is incomplete or compromised, real-time satellite imagery provides complementary large-scale monitoring, enabling terrain mapping and deformation detection. Edge AI facilitates immediate local processing of heterogeneous sensor data, reducing latency and enabling rapid alerts for imminent landslide threats. Quantum computing enhances this framework by accelerating model training, optimizing complex predictive algorithms, and enabling efficient analysis of massive satellite and geospatial datasets. By synergistically combining these technologies, the proposed framework achieves a scalable, intelligent, and adaptive landslide prediction system capable of real-time forecasting. The review examines state-of-the-art advancements in IoT-enabled monitoring, satellite-based observation, edge AI analytics, and quantum-assisted computation, highlighting their individual contributions, integration strategies, and limitations. Critical challenges such as sensor reliability, data fusion complexities, computational constraints, and quantum hardware maturity are discussed alongside potential mitigation strategies. This integrated approach offers the potential to substantially improve landslide prediction accuracy, reduce response time, and support proactive disaster management, representing a transformative step toward intelligent early-warning infrastructures.