Socio-Technological Integration Model For Landslide Early Detection In Supporting Community Resilience: A Literature Review
Keywords:
Landslide early detection; socio-technological integration; community resilience; community-based early warning system; community empowerment
Abstract
Landslides constitute disasters with significant impacts on lives, infrastructure, and the environment, particularly in regions with complex topography and high rainfall. The Indonesian National Disaster Management Agency (BNPB) recorded 2,726 disaster incidents in 2025, with landslides representing one of the primary threats. Early detection has become crucial for reducing casualty risks and losses. This study aims to analyze socio-technological integration models in landslide early detection systems to strengthen community resilience. The methodology employed is a systematic literature review of 34 studies encompassing qualitative, quantitative, and mixed-method approaches from 2010-2025. Bibliometric analysis through VOSviewer using data from Publish or Perish revealed 62 articles with a total of 2,563 citations, an h-index of 19, and a g-index of 50, indicating high scientific relevance. Network, overlay, and density visualizations were utilized to identify patterns and interrelationships among topics. Results demonstrate that integrating detection technologies such as sensors, machine learning, remote sensing, and LIDAR technology with community empowerment through education, community task force formation, and evacuation simulations produces systems that are more effective and sustainable compared to technology-based approaches alone. Key findings include the utilization of simple, easily-operated technology, the importance of village preparedness teams as sustainability drivers, rapid and accessible communication systems, and synergy between local knowledge and scientific data. Studies in Indonesia, South Asia, and globally demonstrate the success of this hybrid approach in local contexts. The socio-technological integration model is proven to enhance community resilience through technical, social, and institutional dimensions. This research confirms that effective landslide early detection requires a holistic approach balancing technological and social aspects, adaptable to various geographical conditions.
References
Alam, M., Manzano, L., Debnath, R., & Ahmed, A. (2024). Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review. Hydrology, 11(8), 111. https://doi.org/10.3390/hydrology11080111
Avinash, K. G., & Ashamanjari, K. G. (2011). Landslide susceptibility modelling of Aghanashini river catchment in Western Ghats of Uttara Kannad District, Karnataka, India. Nature, Environment and Pollution Technology, 10(2), 251-254. Retrieved from https://neptjournal.com/upload-images/NL-32-17-(17)B-1585com.pdf
BNPB. Rangkuman Bencana tahun 2025. Retrieved from https://gis.bnpb.go.id/arcgis/apps/sites/#/public/pages/bencana-besar-tahun-2025
Casagli, N., Intrieri, E., Tofani, V., Gigli, G., & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4, 51-64. https://doi.org/10.1038/s43017-022-00373-x
Chen, Y., You, W., Ou, L., & Tang, H. (2025). A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience. Systems and Soft Computing, 200269. https://doi.org/10.1016/j.sasc.2025.200269
Cieslik, K., Shakya, P., Uprety, M., Dewulf, A., Russell, C., Clark, J., Dhital, M.R., & Dhakal, A. (2019). Building Resilience to Chronic Landslide Hazard Through Citizen Science. Frontiers in Earth Science, 7, Article 278. https://doi.org/10.3389/feart.2019.00278
Coccia, S., Heib, A., & Klein, E. (2022). Combination of UAV-borne LiDAR and UAV-borne photogrammetry to assess slope stability. World Congress on Civil, Structural, and Environmental Engineering. https://doi.org/10.11159/icgre22.107
Cohen, O., Goldberg, A., Lahad, M., & Aharonson-Daniel, L. (2017). Building resilience: The relationship between information provided by municipal authorities during emergency situations and community resilience. Technological Forecasting and Social Change, 121, 119-125. https://doi.org/10.1016/j.techfore.2017.03.021
Corsini, A., Ronchetti, D., Cervi, S., Bonacini, D., Ronchetti, F., & Capra, C. (2020). A Low-Cost Landslide Monitoring and Early Warning System Based on Open Source Hardware and Software. Geosciences, 10(9), Article 309. https://doi.org/10.3390/geosciences10090309
Cvetković, V. M., & Šišović, V. (2024). Community Disaster Resilience in Serbia. Scientific-Professional Society for Disaster Risk Management, Belgrade. Retrieved from https://www.researchgate.net/profile/Vladimir-Cvetkovic-4/publication/379953858_Community_Disaster_Resilience_in_Serbia/links/6760293a2d60b863e2707dc9/Community-Disaster-Resilience-in-Serbia.pdf
Dai, X., Chen, J., Zhang, T., & Xue, C. (2025). Integrated Landslide Risk Assessment via a Landslide Susceptibility Model Based on Intelligent Optimization Algorithms. Remote Sensing, 17(3), Article 545. https://doi.org/10.3390/rs17030545
Díaz, P., & Aedo, I. (2023). Technology-Mediated Citizen Participation in Emergency Management: Balancing Social Capital and Efficacy. In IFIP Conference on Human-Computer Interaction (pp. 44-56). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-61698-3_4
Dixon, N., Smith, A., & Pietz, M. (2022). A community-operated landslide early warning approach: Myanmar case study. Geoenvironmental Disasters, 9, 1-15. https://doi.org/10.1186/s40677-022-00220-7
Dixon, N., Smith, A., Flint, J., Khanna, R., Clark, B., & Andjelkovic, M. (2018). An acoustic emission landslide early warning system for communities in low-income and middle-income countries. Landslides, 15, 1631-1644. https://doi.org/10.1007/s10346-018-0977-1
Fathani, T., Karnawati, D., & Wilopo, W. (2016). An integrated methodology to develop a standard for landslide early warning systems. Natural Hazards and Earth System Sciences, 16, 2123-2135. https://doi.org/10.5194/nhess-16-2123-2016
Ganapathy, G., Nath, A., Zaalishvili, V., & Burdzieva, O. (2023). Building sustainable and resilient communities with landslide early warning systems: A review of best practices. Sustainable Development of Mountain Territories, 15(4), 1007-1022. https://doi.org/10.21177/1998-4502-2023-15-4-1007-1022
Gidon, J., Borah, J., Sahoo, S., Majumdar, S., & Fujita, M. (2024). Bidirectional LSTM Model for Accurate and Real-Time Landslide Detection: A Case Study in Mawiongrim, Meghalaya, India. IEEE Internet of Things Journal, 11, 3792-3800. https://doi.org/10.1109/jiot.2023.3326203
Gorum, T. (2019). Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Engineering Geology, 258, 105155. https://doi.org/10.1016/j.enggeo.2019.105155
Gumiran, B., & Daag, A. (2021). Negotiated participatory action research for multi-stakeholder implementation of early warning systems for landslides. International Journal of Disaster Risk Reduction, 59, Article 102184. https://doi.org/10.1016/j.ijdrr.2021.102184
Hadiana, A., Melina, & Renaldi, F. (2020). IoT-based Disaster Management: A Case of Technological Mitigation in Indonesia. Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10-14, 2020. Retrieved from https://www.ieomsociety.org/detroit2020/papers/405.pdf
Hirye, M.C., Alves, D.S., Filardo Jr, A.S., McPhearson, T., & Wagner, F. (2023). Assessing Landslide Drivers in Social–Ecological–Technological Systems: The Case of Metropolitan Region of São Paulo, Brazil. Remote Sensing, 15(12), 3048. https://doi.org/10.3390/rs15123048
Hostettler, S., Jöhr, A., Monte, C., & D'Acunzi, A. (2019). Community based landslide risk reduction: A review of a Red Cross soil bioengineering for Resilience program in Honduras. Landslides, 16, 1779-1791. https://doi.org/10.1007/s10346-019-01161-3
Huang, X., Song, Y., & Hu, X. (2021). Deploying spatial data for coastal community resilience: A review from the managerial perspective. International Journal of Environmental Research and Public Health, 18(2), Article 830. https://doi.org/10.3390/ijerph18020830
Intrieri, E., Bardi, F., Fanti, R., Gigli, G., Fidolini, F., Casagli, N., Costanzo, S., Raffo, A., Massa, G., Capparelli, G., & Versace, P. (2017). Big data managing in a landslide early warning system: Experience from a ground-based interferometric radar application. Natural Hazards and Earth System Sciences, 17, 1713-1723. https://doi.org/10.5194/nhess-17-1713-2017
Karnawati, D., Fathani, T., & Andayani, B. (2013). The Hybrid Socio-Technical System for Strategic Landslide Disaster Risk Reduction in Indonesia. In: Landslide Science and Practice (pp. 323-331). Springer. https://doi.org/10.1007/978-3-642-22087-6_23
Karnawati, D., Fathani, T., Wilopo, W., & Maarif, S. (2018). TXT-tool 4.062-1.1: A Socio-technical Approach for Landslide Mitigation and Risk Reduction. In: Advancing Culture of Living with Landslides (pp. 621-630). Springer. https://doi.org/10.1007/978-3-319-57777-7_38
Karnawati, D., Fathani, T., Wilopo, W., Setianto, A., & Andayani, B. (2011). Promoting the hybrid socio-technical approach for effective disaster risk reduction in developing countries. WIT Transactions on the Built Environment, 119, 175-182. https://doi.org/10.2495/dman110161
Lelli, F., Mulas, M., Critelli, V., Fabbiani, C., Tondo, M., Aleotti, M., & Corsini, A. (2025). Leveraging High-Frequency UAV–LiDAR Surveys to Monitor Earthflow Dynamics—The Baldiola Landslide Case Study. Remote Sensing, 17(15), 2657. https://doi.org/10.3390/rs17152657
Li, S., Wu, X., Sun, F., Yang, J., & Li, J. (2022). Environmental geological problems in Southwest China: A case study from the researches of regional landslide hazards. Nature Environment and Pollution Technology, 21(1), 159-165. ginal Research Paper https://doi.org/10.46488/NEPT.2022.v21i01.01
Li, X., Wang, S., Fan, Z., Lei, L., Wang, L., He, Y., Cheng, L., & Deng, N. (2025). Front-edge erosion impact on landslide stability: A multi-scale monitoring and numerical simulation case study. PLOS One, 20, e0326223. https://doi.org/10.1371/journal.pone.0326223
Lin, S., Liang, Z., Guo, H., Hu, Q., Cao, X., & Zheng, H. (2025). Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review. Artificial Intelligence Review, 58(6), 168. https://doi.org/10.1007/s10462-025-11106-3
Lv, J., Lu, C., Ye, M., Long, Y., Li, W., & Yang, M. (2025). Enhanced Landslide Visualization and Trace Identification Using LiDAR-Derived DEM. Sensors, 25, 4391. https://doi.org/10.3390/s25144391
Mohanty, A., Hussain, M., Mishra, M., Kattel, D., & Pal, I. (2019). Exploring community resilience and early warning solution for flash floods, debris flow and landslides in conflict prone villages of Badakhshan, Afghanistan. International Journal of Disaster Risk Reduction, 33, 5-15. https://doi.org/10.1016/j.ijdrr.2018.07.012
Muktiyanto, D., Widagdo, S., Istiqomah, M., & Parmawati, R. (2025). Penta Helix Collaboration Model Involving Reserve Component Personnel in Disaster Resilience in Malang Regency. Nature Environment and Pollution Technology, 24(1), 1-12. https://doi.org/10.46488/NEPT.2024.v24i01.D1665
Nanehkaran, Y., Chen, B., Cemiloglu, A., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. (2023). Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals. Water, 15(15), Article 2707. https://doi.org/10.3390/w15152707
Omprakash, N., S., & Balan, D. (2025). Qualitative Analysis on Early Warning Systems on Land Slide through Sensor Technology and Environmental Monitoring in Disaster Preparedness. International Journal of Innovative Science and Research Technology. https://doi.org/10.38124/ijisrt/25apr742
Pawar, N.S., & Sharma, K.V. (2025). Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions. Natural Hazards, 1-35. https://doi.org/10.1007/s11069-024-07106-3
Pellicani, R., Argentiero, I., Manzari, P., Spilotro, G., Marzo, C., Ermini, R., & Apollonio, C. (2019). UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy). Geosciences, 9(6), 248. https://doi.org/10.3390/geosciences9060248
Pradhan, B., Al-Najjar, H., Sameen, M., Mezaal, M., & Alamri, A. (2020). Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos. IEEE Access, 8, 121942-121954. https://doi.org/10.1109/access.2020.3006914
Prarikeslan, W., Sudiar, N. Y., Anugrah, G., Beri, D., Handayani, D., Eka Putri, I. L., & Gautama, M. I. (2023). The Impact of Climate Change on the City of Padang, Indonesia. Nature Environment & Pollution Technology, 22(4). https://doi.org/10.46488/NEPT.2023.v22i04.050
Rahayu, H.P., Comfort, L.K., Haigh, R., Amaratunga, D., & Khoirunnisa, D. (2020). A study of people-centered early warning system in the face of near-field tsunami risk for Indonesian coastal cities. International Journal of Disaster Resilience in the Built Environment, 11(2), 241-262. https://doi.org/10.1108/IJDRBE-10-2019-0068
Sestraș, P., Badea, G., Badea, A., Sălăgean, T., Oniga, V., Roșca, S., Bilașco, Ș., Bruma, S., Spalević, V., Kader, S., Billi, P., & Nedevschi, S. (2024). A novel method for landslide deformation monitoring by fusing UAV photogrammetry and LiDAR data based on each sensor's mapping advantage in regards to terrain feature. Engineering Geology, 338, 107890. https://doi.org/10.1016/j.enggeo.2024.107890
Singh, A., Dhoriyani, U., Maiti, R., Panpalia, G., Dalal, V., & Dash, D. (2024). Early Landslide Detection Using Wireless Sensor Network. In: 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-8). https://doi.org/10.1109/aisp61711.2024.10870788
Strzabala, K., Ćwiąkała, P., & Puniach, E. (2024). Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing. Remote Sensing, 16(15), Article 2781. https://doi.org/10.3390/rs16152781
Suharini, E., Asiah, S., & Kurniawan, E. (2020). The Role of Community-Based Disaster Preparedness and Response Team in Building Community Resilience. GEOGRAFIA Online Malaysian Journal of Society and Space, 16(4), 30-44. https://doi.org/10.17576/geo-2020-1604-03
Thapa, N., & Sharma, S. (2024). Integrating Machine Learning and Community-Based Approaches for Enhanced Early Warning Systems in Cascading Hazard Zones: A Case Study from Melamchi, Nepal. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4964914
Tran, D.T., Nguyen, D.C., Tran, D.N., & Ta, D.T. (2015). Development of a rainfall-triggered landslide system using wireless accelerometer network. International Journal of Advancements in Computing Technology (IJACT), 7(5), 14-23. http://eprints.uet.vnu.edu.vn/eprints/id/eprint/1750
Van Westen, C.J. (2000). The Modelling of Landslide Hazards Using GIS. Surveys in Geophysics, 21, 241-255. https://doi.org/10.1023/A:1006794127521
Vimalnath, S., Kavika, V., Kirubasakthi, A., Madhumitha, K., & Magudeeshwari, V. (2025, April). Design a Real-Time Landslide Detection and Alerting System for Early Warning. In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM) (pp. 1546-1552). IEEE. https://doi.org/10.1109/ictmim65579.2025.10988374
Wang, Y., Wen, H., Sun, D., & Li, Y. (2021). Quantitative assessment of landslide risk based on susceptibility mapping using random forest and GeoDetector. Remote Sensing, 13(13), 2625.https://doi.org/10.3390/rs13132625
Werthmann, C., Sapena, M., Kühnl, M., Singer, J., Garcia, C., Breuninger, T., Gamperl, M., Menschik, B., Schäfer, H., Schröck, S., Seiler, L., Thuro, K., & Taubenböck, H. (2024). Insights into the development of a landslide early warning system prototype in an informal settlement: The case of Bello Oriente in Medellín, Colombia. Natural Hazards and Earth System Sciences, 24, 1843-1869. https://doi.org/10.5194/nhess-24-1843-2024
Wu, D., Liang, T., Yang, Y., Pei, Q., Yi, Y., Wu, J., & Li, D. (2025). Experimental study on displacement monitoring of instable highway slope based on MEMS sensors. Frontiers in Earth Science, 12, 1541217. https://doi.org/10.3389/feart.2025.1541217
Wu, P. F. (2011). A mixed methods approach to technology acceptance research. Journal of the AIS.https://ssrn.com/abstract=1937656
Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J., & Travé, A. (2021). Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China. Frontiers in Earth Science, 9, Article 722491. https://doi.org/10.3389/feart.2021.722491
Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., & Liu, X. (2021). Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China. Remote Sensing, 13, 4234. https://doi.org/10.3390/rs13214234
Zhou, C., Yin, K., Cao, Y., Intrieri, E., Ahmed, B., & Catani, F. (2018). Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides, 15, 2211-2225. https://doi.org/10.1007/s10346-018-1022-0
Avinash, K. G., & Ashamanjari, K. G. (2011). Landslide susceptibility modelling of Aghanashini river catchment in Western Ghats of Uttara Kannad District, Karnataka, India. Nature, Environment and Pollution Technology, 10(2), 251-254. Retrieved from https://neptjournal.com/upload-images/NL-32-17-(17)B-1585com.pdf
BNPB. Rangkuman Bencana tahun 2025. Retrieved from https://gis.bnpb.go.id/arcgis/apps/sites/#/public/pages/bencana-besar-tahun-2025
Casagli, N., Intrieri, E., Tofani, V., Gigli, G., & Raspini, F. (2023). Landslide detection, monitoring and prediction with remote-sensing techniques. Nature Reviews Earth & Environment, 4, 51-64. https://doi.org/10.1038/s43017-022-00373-x
Chen, Y., You, W., Ou, L., & Tang, H. (2025). A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience. Systems and Soft Computing, 200269. https://doi.org/10.1016/j.sasc.2025.200269
Cieslik, K., Shakya, P., Uprety, M., Dewulf, A., Russell, C., Clark, J., Dhital, M.R., & Dhakal, A. (2019). Building Resilience to Chronic Landslide Hazard Through Citizen Science. Frontiers in Earth Science, 7, Article 278. https://doi.org/10.3389/feart.2019.00278
Coccia, S., Heib, A., & Klein, E. (2022). Combination of UAV-borne LiDAR and UAV-borne photogrammetry to assess slope stability. World Congress on Civil, Structural, and Environmental Engineering. https://doi.org/10.11159/icgre22.107
Cohen, O., Goldberg, A., Lahad, M., & Aharonson-Daniel, L. (2017). Building resilience: The relationship between information provided by municipal authorities during emergency situations and community resilience. Technological Forecasting and Social Change, 121, 119-125. https://doi.org/10.1016/j.techfore.2017.03.021
Corsini, A., Ronchetti, D., Cervi, S., Bonacini, D., Ronchetti, F., & Capra, C. (2020). A Low-Cost Landslide Monitoring and Early Warning System Based on Open Source Hardware and Software. Geosciences, 10(9), Article 309. https://doi.org/10.3390/geosciences10090309
Cvetković, V. M., & Šišović, V. (2024). Community Disaster Resilience in Serbia. Scientific-Professional Society for Disaster Risk Management, Belgrade. Retrieved from https://www.researchgate.net/profile/Vladimir-Cvetkovic-4/publication/379953858_Community_Disaster_Resilience_in_Serbia/links/6760293a2d60b863e2707dc9/Community-Disaster-Resilience-in-Serbia.pdf
Dai, X., Chen, J., Zhang, T., & Xue, C. (2025). Integrated Landslide Risk Assessment via a Landslide Susceptibility Model Based on Intelligent Optimization Algorithms. Remote Sensing, 17(3), Article 545. https://doi.org/10.3390/rs17030545
Díaz, P., & Aedo, I. (2023). Technology-Mediated Citizen Participation in Emergency Management: Balancing Social Capital and Efficacy. In IFIP Conference on Human-Computer Interaction (pp. 44-56). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-61698-3_4
Dixon, N., Smith, A., & Pietz, M. (2022). A community-operated landslide early warning approach: Myanmar case study. Geoenvironmental Disasters, 9, 1-15. https://doi.org/10.1186/s40677-022-00220-7
Dixon, N., Smith, A., Flint, J., Khanna, R., Clark, B., & Andjelkovic, M. (2018). An acoustic emission landslide early warning system for communities in low-income and middle-income countries. Landslides, 15, 1631-1644. https://doi.org/10.1007/s10346-018-0977-1
Fathani, T., Karnawati, D., & Wilopo, W. (2016). An integrated methodology to develop a standard for landslide early warning systems. Natural Hazards and Earth System Sciences, 16, 2123-2135. https://doi.org/10.5194/nhess-16-2123-2016
Ganapathy, G., Nath, A., Zaalishvili, V., & Burdzieva, O. (2023). Building sustainable and resilient communities with landslide early warning systems: A review of best practices. Sustainable Development of Mountain Territories, 15(4), 1007-1022. https://doi.org/10.21177/1998-4502-2023-15-4-1007-1022
Gidon, J., Borah, J., Sahoo, S., Majumdar, S., & Fujita, M. (2024). Bidirectional LSTM Model for Accurate and Real-Time Landslide Detection: A Case Study in Mawiongrim, Meghalaya, India. IEEE Internet of Things Journal, 11, 3792-3800. https://doi.org/10.1109/jiot.2023.3326203
Gorum, T. (2019). Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Engineering Geology, 258, 105155. https://doi.org/10.1016/j.enggeo.2019.105155
Gumiran, B., & Daag, A. (2021). Negotiated participatory action research for multi-stakeholder implementation of early warning systems for landslides. International Journal of Disaster Risk Reduction, 59, Article 102184. https://doi.org/10.1016/j.ijdrr.2021.102184
Hadiana, A., Melina, & Renaldi, F. (2020). IoT-based Disaster Management: A Case of Technological Mitigation in Indonesia. Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10-14, 2020. Retrieved from https://www.ieomsociety.org/detroit2020/papers/405.pdf
Hirye, M.C., Alves, D.S., Filardo Jr, A.S., McPhearson, T., & Wagner, F. (2023). Assessing Landslide Drivers in Social–Ecological–Technological Systems: The Case of Metropolitan Region of São Paulo, Brazil. Remote Sensing, 15(12), 3048. https://doi.org/10.3390/rs15123048
Hostettler, S., Jöhr, A., Monte, C., & D'Acunzi, A. (2019). Community based landslide risk reduction: A review of a Red Cross soil bioengineering for Resilience program in Honduras. Landslides, 16, 1779-1791. https://doi.org/10.1007/s10346-019-01161-3
Huang, X., Song, Y., & Hu, X. (2021). Deploying spatial data for coastal community resilience: A review from the managerial perspective. International Journal of Environmental Research and Public Health, 18(2), Article 830. https://doi.org/10.3390/ijerph18020830
Intrieri, E., Bardi, F., Fanti, R., Gigli, G., Fidolini, F., Casagli, N., Costanzo, S., Raffo, A., Massa, G., Capparelli, G., & Versace, P. (2017). Big data managing in a landslide early warning system: Experience from a ground-based interferometric radar application. Natural Hazards and Earth System Sciences, 17, 1713-1723. https://doi.org/10.5194/nhess-17-1713-2017
Karnawati, D., Fathani, T., & Andayani, B. (2013). The Hybrid Socio-Technical System for Strategic Landslide Disaster Risk Reduction in Indonesia. In: Landslide Science and Practice (pp. 323-331). Springer. https://doi.org/10.1007/978-3-642-22087-6_23
Karnawati, D., Fathani, T., Wilopo, W., & Maarif, S. (2018). TXT-tool 4.062-1.1: A Socio-technical Approach for Landslide Mitigation and Risk Reduction. In: Advancing Culture of Living with Landslides (pp. 621-630). Springer. https://doi.org/10.1007/978-3-319-57777-7_38
Karnawati, D., Fathani, T., Wilopo, W., Setianto, A., & Andayani, B. (2011). Promoting the hybrid socio-technical approach for effective disaster risk reduction in developing countries. WIT Transactions on the Built Environment, 119, 175-182. https://doi.org/10.2495/dman110161
Lelli, F., Mulas, M., Critelli, V., Fabbiani, C., Tondo, M., Aleotti, M., & Corsini, A. (2025). Leveraging High-Frequency UAV–LiDAR Surveys to Monitor Earthflow Dynamics—The Baldiola Landslide Case Study. Remote Sensing, 17(15), 2657. https://doi.org/10.3390/rs17152657
Li, S., Wu, X., Sun, F., Yang, J., & Li, J. (2022). Environmental geological problems in Southwest China: A case study from the researches of regional landslide hazards. Nature Environment and Pollution Technology, 21(1), 159-165. ginal Research Paper https://doi.org/10.46488/NEPT.2022.v21i01.01
Li, X., Wang, S., Fan, Z., Lei, L., Wang, L., He, Y., Cheng, L., & Deng, N. (2025). Front-edge erosion impact on landslide stability: A multi-scale monitoring and numerical simulation case study. PLOS One, 20, e0326223. https://doi.org/10.1371/journal.pone.0326223
Lin, S., Liang, Z., Guo, H., Hu, Q., Cao, X., & Zheng, H. (2025). Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review. Artificial Intelligence Review, 58(6), 168. https://doi.org/10.1007/s10462-025-11106-3
Lv, J., Lu, C., Ye, M., Long, Y., Li, W., & Yang, M. (2025). Enhanced Landslide Visualization and Trace Identification Using LiDAR-Derived DEM. Sensors, 25, 4391. https://doi.org/10.3390/s25144391
Mohanty, A., Hussain, M., Mishra, M., Kattel, D., & Pal, I. (2019). Exploring community resilience and early warning solution for flash floods, debris flow and landslides in conflict prone villages of Badakhshan, Afghanistan. International Journal of Disaster Risk Reduction, 33, 5-15. https://doi.org/10.1016/j.ijdrr.2018.07.012
Muktiyanto, D., Widagdo, S., Istiqomah, M., & Parmawati, R. (2025). Penta Helix Collaboration Model Involving Reserve Component Personnel in Disaster Resilience in Malang Regency. Nature Environment and Pollution Technology, 24(1), 1-12. https://doi.org/10.46488/NEPT.2024.v24i01.D1665
Nanehkaran, Y., Chen, B., Cemiloglu, A., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. (2023). Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals. Water, 15(15), Article 2707. https://doi.org/10.3390/w15152707
Omprakash, N., S., & Balan, D. (2025). Qualitative Analysis on Early Warning Systems on Land Slide through Sensor Technology and Environmental Monitoring in Disaster Preparedness. International Journal of Innovative Science and Research Technology. https://doi.org/10.38124/ijisrt/25apr742
Pawar, N.S., & Sharma, K.V. (2025). Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions. Natural Hazards, 1-35. https://doi.org/10.1007/s11069-024-07106-3
Pellicani, R., Argentiero, I., Manzari, P., Spilotro, G., Marzo, C., Ermini, R., & Apollonio, C. (2019). UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy). Geosciences, 9(6), 248. https://doi.org/10.3390/geosciences9060248
Pradhan, B., Al-Najjar, H., Sameen, M., Mezaal, M., & Alamri, A. (2020). Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos. IEEE Access, 8, 121942-121954. https://doi.org/10.1109/access.2020.3006914
Prarikeslan, W., Sudiar, N. Y., Anugrah, G., Beri, D., Handayani, D., Eka Putri, I. L., & Gautama, M. I. (2023). The Impact of Climate Change on the City of Padang, Indonesia. Nature Environment & Pollution Technology, 22(4). https://doi.org/10.46488/NEPT.2023.v22i04.050
Rahayu, H.P., Comfort, L.K., Haigh, R., Amaratunga, D., & Khoirunnisa, D. (2020). A study of people-centered early warning system in the face of near-field tsunami risk for Indonesian coastal cities. International Journal of Disaster Resilience in the Built Environment, 11(2), 241-262. https://doi.org/10.1108/IJDRBE-10-2019-0068
Sestraș, P., Badea, G., Badea, A., Sălăgean, T., Oniga, V., Roșca, S., Bilașco, Ș., Bruma, S., Spalević, V., Kader, S., Billi, P., & Nedevschi, S. (2024). A novel method for landslide deformation monitoring by fusing UAV photogrammetry and LiDAR data based on each sensor's mapping advantage in regards to terrain feature. Engineering Geology, 338, 107890. https://doi.org/10.1016/j.enggeo.2024.107890
Singh, A., Dhoriyani, U., Maiti, R., Panpalia, G., Dalal, V., & Dash, D. (2024). Early Landslide Detection Using Wireless Sensor Network. In: 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-8). https://doi.org/10.1109/aisp61711.2024.10870788
Strzabala, K., Ćwiąkała, P., & Puniach, E. (2024). Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing. Remote Sensing, 16(15), Article 2781. https://doi.org/10.3390/rs16152781
Suharini, E., Asiah, S., & Kurniawan, E. (2020). The Role of Community-Based Disaster Preparedness and Response Team in Building Community Resilience. GEOGRAFIA Online Malaysian Journal of Society and Space, 16(4), 30-44. https://doi.org/10.17576/geo-2020-1604-03
Thapa, N., & Sharma, S. (2024). Integrating Machine Learning and Community-Based Approaches for Enhanced Early Warning Systems in Cascading Hazard Zones: A Case Study from Melamchi, Nepal. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4964914
Tran, D.T., Nguyen, D.C., Tran, D.N., & Ta, D.T. (2015). Development of a rainfall-triggered landslide system using wireless accelerometer network. International Journal of Advancements in Computing Technology (IJACT), 7(5), 14-23. http://eprints.uet.vnu.edu.vn/eprints/id/eprint/1750
Van Westen, C.J. (2000). The Modelling of Landslide Hazards Using GIS. Surveys in Geophysics, 21, 241-255. https://doi.org/10.1023/A:1006794127521
Vimalnath, S., Kavika, V., Kirubasakthi, A., Madhumitha, K., & Magudeeshwari, V. (2025, April). Design a Real-Time Landslide Detection and Alerting System for Early Warning. In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM) (pp. 1546-1552). IEEE. https://doi.org/10.1109/ictmim65579.2025.10988374
Wang, Y., Wen, H., Sun, D., & Li, Y. (2021). Quantitative assessment of landslide risk based on susceptibility mapping using random forest and GeoDetector. Remote Sensing, 13(13), 2625.https://doi.org/10.3390/rs13132625
Werthmann, C., Sapena, M., Kühnl, M., Singer, J., Garcia, C., Breuninger, T., Gamperl, M., Menschik, B., Schäfer, H., Schröck, S., Seiler, L., Thuro, K., & Taubenböck, H. (2024). Insights into the development of a landslide early warning system prototype in an informal settlement: The case of Bello Oriente in Medellín, Colombia. Natural Hazards and Earth System Sciences, 24, 1843-1869. https://doi.org/10.5194/nhess-24-1843-2024
Wu, D., Liang, T., Yang, Y., Pei, Q., Yi, Y., Wu, J., & Li, D. (2025). Experimental study on displacement monitoring of instable highway slope based on MEMS sensors. Frontiers in Earth Science, 12, 1541217. https://doi.org/10.3389/feart.2025.1541217
Wu, P. F. (2011). A mixed methods approach to technology acceptance research. Journal of the AIS.https://ssrn.com/abstract=1937656
Xing, Y., Yue, J., Guo, Z., Chen, Y., Hu, J., & Travé, A. (2021). Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China. Frontiers in Earth Science, 9, Article 722491. https://doi.org/10.3389/feart.2021.722491
Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., & Liu, X. (2021). Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China. Remote Sensing, 13, 4234. https://doi.org/10.3390/rs13214234
Zhou, C., Yin, K., Cao, Y., Intrieri, E., Ahmed, B., & Catani, F. (2018). Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides, 15, 2211-2225. https://doi.org/10.1007/s10346-018-1022-0
Published
2025-12-20
How to Cite
Kartiko Ardi Widodo, Aryuanto Soetedjo, & Iftitah Ruwana. (2025). Socio-Technological Integration Model For Landslide Early Detection In Supporting Community Resilience: A Literature Review. International Journal of Technology and Education Research, 3(04), 902-924. https://doi.org/10.63922/ijeter.v3i04.2710
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