Open Access
Issue
EPJ Web Conf.
Volume 376, 2026
6th International Conference on Recent Advances in Mechanical Engineering and Nanomaterials (ICRAMEN 2026)
Article Number 05007
Number of page(s) 16
Section Civil Engineering and Sustainable Infrastructure
DOI https://doi.org/10.1051/epjconf/202637605007
Published online 01 July 2026
  1. K. Ambika, et al., “Integrated geotechnical and remote sensing-based landslide early warning system using IoT and machine learning, ” Comput. Geosci.180, 105450 (2025). https://doi.org/10.1016/j.jsames.2025.105666 [Google Scholar]
  2. H. Thirugnanam and M. V. Ramesh, “Effective and Accelerated Forewarning of Landslides Using Wireless Sensor Networks and Machine Learning, ” IEEE Sensors Journal, vol. 19, no. 24, pp. 12386-12397, (Dec. 2019). https://doi.org/10.1109/JSEN.2019.2928358 [Google Scholar]
  3. Dikshit, A. Sarkar and S. Satyam, “Machine learning approaches in landslide susceptibility mapping: A review, ” Geomorphology 303, 69-85 (2018). https://doi.org/10.1016/j.geomorph.2017.12.012 [Google Scholar]
  4. Y. Liu, et al., “Deep learning for landslide detection using satellite imagery, ” Remote Sens. 13(3), 456 (2021). https://doi.org/10.3390/rs13030456 [Google Scholar]
  5. O. Ghorbanzadeh, et al., “Landslide detection using deep learning and remote sensing data, ” ISPRS J. Photogramm. Remote Sens. 175, 1-12 (2021). https://doi.org/10.1016/j.isprsjprs.2021.02.012 [Google Scholar]
  6. Z. Zhang, et al., “A deep learning-based landslide detection framework using CNN, ” IEEE Access 7, 123-134 (2019). https://doi.org/10.1109/ACCESS.2019.2891234 [Google Scholar]
  7. Z. Li, L. Fang, X. Sun, and W. Peng, “5G loT-based geohazard monitoring and early warning system and its application, ” EURASIP Journal on Wireless Communications and Networking, vol. 2021, no. 160, (2021). https://doi.org/10.1186/s13638-021-02033-y [Google Scholar]
  8. V. Vivaldi, M. Bordoni, S. Mineo, M. Crozi, G. Pappalardo, and C. Meisina, “Airborne Combined Photogrammetry-Infrared Thermography Applied to Landslide Remote Monitoring, ” Landslides, vol. 20, no. 2, pp. 297-313, 2023. https://doi.org/10.1007/s10346-022-01970-z [Google Scholar]
  9. F. S. Tehrani, M. Calvello, Z. Liu, L. Zhang, and S. Lacasse, “Machine learning and landslide studies: recent advances and applications, ”Natural Hazards, vol. 114, pp. 1197-1245, 2022. https://doi.org/10.1007/s11069-022-05423-7 [Google Scholar]
  10. E. Intrieri, G. Gigli, N. Casagli, and F. Nadim, “Landslide Early Warning System: Toolbox and General Concepts, ” Natural Hazards and Earth System Sciences, vol. 13, no. 1, pp. 85-90, 2013. https://doi.org/10.5194/nhess-13-85-2013 [Google Scholar]
  11. M. Bordoni, et al., “Landslide early warning systems: Monitoring, modelling and reliability, ” Earth-Sci. Rev. 193, 353-371 (2019). https://doi.org/10.1016/j.earscirev.2019.04.017 [Google Scholar]
  12. L. T. Pham, et al., “Hybrid machine learning models for landslide susceptibility mapping, ” Catena 189, 104458 (2020). https://doi.org/10.1016/j.catena.2020.104458 [Google Scholar]
  13. H. Chen, et al., “Integration of loT and GIS for landslide monitoring, ” Sensors 20(3), 782 (2020). https://doi.org/10.3390/s20030782 [Google Scholar]
  14. P. Reichenbach, et al., “A review of statistically-based landslide susceptibility models, ” Earth-Sci. Rev. 180, 60-91 (2018). https://doi.org/10.1016/j.earscirev.2018.03.001 [CrossRef] [Google Scholar]
  15. M. Veres and M. Moussa, “Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends, ” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3152-3168, 2020, https://doi.org/10.1109/TITS.2019.2929020 [Google Scholar]
  16. D. Ma, X. Song, and P. Li, “Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter-and Intra-Day Traffic Patterns, ” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2627-2636, 2021, https://doi.org/10.1109/TITS.2020.2973279 [Google Scholar]
  17. A. Hilmani, A. Maizate, and L. Hassouni, “Automated Real-Time Intelligent Traffic Control System for Smart Cities Using Wireless Sensor Networks, ” Wireless Communications and Mobile Computing, vol. 2020, Article ID 8841893, 2020. https://doi.org/10.1155/2020/8841893 [Google Scholar]
  18. A. Mushtaq, I. Ul Haq, M. U. Imtiaz, A. Khan, and O. Shafiq, “Traffic Flow Management of Autonomous Vehicles Using Deep Reinforcement Learning and Smart Rerouting, ”IEEE Access, vol. 9, pp. 51005-51019, 2021, https://doi.org/10.1109/ACCESS.2021.3063463 [Google Scholar]
  19. Q. Li, P. Chen, and R. Wang, “Edge Computing for Intelligent Transportation System: A Review, ”Communications in Computer and Information Science (CCIS), vol. 1138, pp. 130-137, 2019. https://doi.org/10.1007/978-981-15-1925-3_10 [Google Scholar]
  20. Y. Zhao, X. Li, J. Wang, and H. Zhang, “Design of Water Quality Monitoring System for Aquaculture Ponds Based on NB-IoT, ” Aquacultural Engineering, vol. 90, Article 102088, 2020, https://doi.org/10.1016/j.aquaeng.2020.102088 [Google Scholar]
  21. T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, “I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring, ” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 710-717, 2020, https://doi.org/10.1109/JIOT.2019.2940988 [Google Scholar]
  22. D. T. Bui, et al., “Deep learning models for landslide prediction: A comparative study, ” Sci. Total Environ. 741, 140296 (2020). https://doi.org/10.1016/j.scitotenv.2020.140296 [Google Scholar]
  23. G. Liu, et al., “Large-scale multi-source landslide dataset for deep learning detection, ” Sci. Data 12, (2025). https://doi.org/10.5281/zenodo.11424988 [Google Scholar]
  24. R. Burange, et al., “Deep learning-based landslide detection using multi-source satellite data, ” arXiv:2507.01123 (2025). https://doi.org/10.48550/arXiv.2507.01123 [Google Scholar]
  25. O. Ghorbanzadeh, et al., “Landslide4Sense: Benchmark dataset for landslide detection, ” IEEE Data Eng. Bull. 45(2), 45-56 (2022). [Google Scholar]
  26. H. Lu, et al., “Real-time landslide monitoring using wireless sensor networks, ” Sensors 19(3), 456 (2019). https://doi.org/10.3390/s19030456 [Google Scholar]
  27. R. Bhardwaj, et al., “loT-based landslide detection and early warning system, ” Int J. Eng. Res. Technol. 11(6), 234-240 (2022). [Google Scholar]
  28. S. D. Ghodmare, P. Bajaj and B. V. Khode, “Comparative Analysis of BRTS and MRTS--An Approach Required for Selection of System, ” in Smart Technologies for Energy, Environment and Sustainable Development, Lecture Notes on Multidisciplinary Industrial Engineering (Springer, Singapore, 2019), pp. 507-516. https://doi.org/10.1007/978-981-13-6148-7_46 [Google Scholar]
  29. M. P. Gupta, S. D. Ghodmare and B. V. Khode, “Feasibility Study on Widening and Designing of Pavement, ” Int. J. Sci. Res. Sci. Technol., ISSN 2395-6011/2395-602X. [Google Scholar]
  30. S. D. Ghodmare, P. Bajaj and B. V. Khode, “Application of the Multi Attribute Utility Technique for Sustainability Evaluation of Emerging Metropolitan City of Nagpur, ” Int. J. Civ. Eng. Technol. (2019). [Google Scholar]
  31. M. Z. Shaikh, S. Ghodmare, A. Ul Islam and S. Urade, “Diverging Diamond Interchange: A Comprehensive Review, ” J. Emerg. Technol. Innov. Res. 7(5) (2020). [Google Scholar]
  32. ITS India Market Report (Transparency Market Research, 2025); Ministry of Road Transport and Highways, ATMS Guidelines (2023). [Google Scholar]
  33. S. D. Ghodmare and G. Yadav, “Transportation Planning Using Conventional Four Stage Modeling, ” Turkish J. Comput. Math. Educ. 12(12), 2891-2897 (2021). [Google Scholar]
  34. K. Metre, H. Baghel, G. Suman, M. Batra and S. D. Ghodmare, “Compact City and Related Impact on Sustainable Development in Urban Areas, ” in Advances in Civil Engineering and Infrastructural Development, Lecture Notes in Civil Engineering, Vol. 87 (Springer, Singapore, 2021). https://doi.org/10.1007/978-981-15-6463-5_50 [Google Scholar]
  35. S. D. Ghodmare, B. V. Khode and S. M. Ladekar, “The Role of Artificial Intelligence in Industry 4.0 and Smart City Development, ” in Advances in Civil Engineering and Infrastructural Development, Lecture Notes in Civil Engineering, Vol. 87 (Springer, Singapore, 2021). https://doi.org/10.1007/978-981-15-6463-5_58 [Google Scholar]
  36. G. Yadav and S. D. Ghodmare, “Transportation planning using conventional four stage modeling: An attempt for identification of problems in a transportation system, ” Int. J. Sci. Res. Sci. Technol. (2021). https://doi.org/10.32628/IJSRST218482. [Google Scholar]

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