Prevention and Defense Technologies for Natural Disasters: A Review
Article Sidebar
Main Article Content
Abstract
Based on the theoretical framework of natural disaster prevention and emergency response systems, this study analyzes the core connotations of "prevention" and "defense" within the pre-disaster prevention and control system for natural disasters, along with their synergistic mechanisms, uncovering their foundational roles in enhancing emergency response effectiveness and reducing disaster losses. The theoretical research results and practical models related to prevention and defense of natural disasters both domestically and internationally, are systematically reviewed in this paper, with a focus on analyzing the performance and existing issues in typical disaster cases, as well as analyzing successful cases and the shortcomings in actual emergency situations, by using literature review and case analysis. Through comparative case analysis, we propose three major research areas for future natural disaster prevention and defense: 1) advancing research on disaster mechanisms and innovating comprehensive prevention and control theories, 2) optimizing emergency management coordination systems and resilient resource allocation, and 3) strengthening technological support and enhancing social collaboration capabilities.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
By submitting a manuscript to Evidence in Earth Science, the author(s) agree to the following terms:
-
Copyright Ownership:
Authors retain copyright to their submitted work. Upon acceptance for publication, the authors grant Evidence in Earth Science a license to publish the work under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). -
Open Access License (CC-BY 4.0):
The articles published in Evidence in Earth Science are open access and distributed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). This means that:- Users are free to share, copy, modify, distribute, and display the work, including for commercial purposes, as long as they provide appropriate attribution to the original author(s).
- The work can be adapted or used in derivative works, but proper credit must be given.
For more detailed information on the licensing terms, please refer to the Creative Commons Attribution 4.0 International License (CC-BY 4.0).
References
Rokhideh M., Fearnley C. and Budimir M., 2025, Multi-hazard early warning systems in the Sendai Framework for Disaster Risk Reduction: Achievements, gaps, and future directions: International Journal of Disaster Risk Science, v. 2025, p. 1-14, https://doi.org/10.1007/S13753-025-00622-9. DOI: https://doi.org/10.1007/s13753-025-00622-9
Wu W., 2023, Working together to reduce risks and create a resilient future - A high-level meeting on the mid-term review of the Sendai Framework for Disaster Reduction: Disaster Reduction in China, v. 13, p. 56-57.
He H. and Hu X., 2021, Status and Enlightenment of Natural Disaster Early Warning in the United States: City and Disaster Reduction, v. 2021, no. 6, p. 59-62.
Liu J., Song L. and Zhang W., 2024, Comparative Analysis and Enlightenment on Disaster Response Between China and Japan:Taking the M6.2 Earthquake Jishishan in China and the M7.4 Noto Peninsula Earthquake in Japan as Examples: City and Disaster Reduction, v. 1, p. 15-21.
Zhu M., 2005, Comprehensive Improvement of National Comprehensive Disaster Reduction Capacity - Overview of the First Plenary Session of the National Disaster Reduction Commission: Disaster Reduction in China, v. 6, p. 6-7.
Kong F., Kang X. and Wang Y., 2024, Consilience mode and optimization path of comprehensive disaster risk governance from an interdisciplinary perspective: Journal of Catastrophology, v. 39, no. 3, p. 1-8.
Li B., Yuan Y. and Zhou M., 2004, Advances in study on natural disaster emergency management in China: Journal of Natural Disasters, v. 2004, no. 3, p. 18-23.
Newman R. and Noy I., 2023, The global costs of extreme weather that are attributable to climate change: Nature Communication, v. 14, no. 1, p. 6103, https://doi.org/10.1038/s41467-023-41888-1. DOI: https://doi.org/10.1038/s41467-023-41888-1
Nohrstedt D., Hileman J., Mazzoleni M., et al., 2022, Exploring disaster impacts on adaptation actions in 549 cities worldwide: Nature Communications, v. 13, p. 3360, https://doi.org/10.1038/s41467-022-31059-z. DOI: https://doi.org/10.1038/s41467-022-31059-z
Xue Z., Xu C., Gao H., et al., 2024, Disaster chain thinking improves the capabilities of disaster prevention, mitigation, and relief in China: Natural Hazards Research, v. 4, no. 2, p. 320-323. DOI: https://doi.org/10.1016/j.nhres.2023.11.013
Cui P. and Guo J., 2021, Evolution Models,Risk Prevention and Control Countermeasures of the Valley Disaster Chain: Advanced Engineering Sciences, v. 53, no. 3, p. 5-18.
Liu C., Liang K. and Wang X., 2024, Cause analysis of disaster chain of soil flow in Dashagou Basin in Jishishan Ms 6.2 magnitude earthquake area: Geological Review, v. 70, no. 3, p. 960-974, https://doi.org/10.16509/j.georeview.2024.04.011.
Yin Y., Li B., Zhang T., et al., 2021, The February 7 of 2021 glacier-rock avalanche and the outburst flooding disaster chain in Chamoli,India: The Chinese Journal of Geological Hazard and Control, v. 32, no. 3, p. 1-8, https://doi.org/10.16031/j.cnki.issn.1003-8035.2021.03-01.
Xue Z., Xu C. and Gao H., 2024, Establishing a chain thinking of earthquakes and geological disasters to enhance disaster prevention, reduction, and relief capabilities: Disaster Reduction in China, v. no. 12, p. 44-47.
Zhang X. and Chen H., 2016, Study on formulating mode of emergency plan for unexpected geological disaster: Yangtze River, v. 47, no. 18, p. 1-4+10, https://doi.org/10.16232/j.cnki.1001-4179.2016.18.001.
Xu C., Shen L. and Wang G., 2016, Soft computing in assessment of earthquake-triggered landslide susceptibility: Environmental Earth Sciences, v. 75, no. 9, p. 767, https://doi.org/10.1007/s12665-016-5576-7. DOI: https://doi.org/10.1007/s12665-016-5576-7
Tang H., 2022, Advance and prospects of major landslides prediction and forecasting: Bulletin of Geological Science and Technology, v. 41, no. 6, p. 1-13.
Zhao G., Liu R., Yang M., et al., 2022, Large-scale flash flood warning in China using deep learning: Journal of Hydrology, v. 604, p. 127222, https://doi.org/10.1016/j.jhydrol.2021.127222. DOI: https://doi.org/10.1016/j.jhydrol.2021.127222
Feng G., 2024, Analysis of the development trend of intelligent disaster reduction technology and emergency rescue equipment: Disaster Reduction in China, v. 14, p. 52-53.
Peng Z., Wu X. and Pan Y., 2024, Typical seismic damage of urban residential buildings in Ms6.8 Luding earthquake and its enlightenment: Building Structure, v. 54, no. 7, p.
Zhang K., 2020, Review on geological disaster monitoring and early warning system based on“3S”technology in China: The Chinese Journal of Geological Hazard and Control, v. 31, no. 6, p. 1-11.
Hu X., Wu S., Zhang G., et al., 2021, Landslide displacement prediction using kinematics-based random forests method: A case study in Jinping Reservoir Area, China: Engineering Geology, v. 283, p. 105975. DOI: https://doi.org/10.1016/j.enggeo.2020.105975
Peng J. and Li Z., 2022, Can geological big data assist in predicting geological hazards?: Earth Science, v. 47, no. 10, p. 3900-3901.
Lu Y. and Li R., 2020, Rebuilding resilient homeland: an NGO-led post-Lushan earthquake experimental reconstruction program: Natural Hazards, v. 104, no. 1, p. 853-882. DOI: https://doi.org/10.1007/s11069-020-04194-3
Guo Z., Moosavi V. and Leitão J. P., 2022, Data-driven rapid flood prediction mapping with catchment generalizability: Journal of Hydrology, v. 609, p. 127726. DOI: https://doi.org/10.1016/j.jhydrol.2022.127726
Ma S., Xu C., Wang T., et al., 2019, Application of two simplified Newmark models to the assessment of landslides triggered by the 2008Wenchuan earthquake: Seismology and Geology, v. 41, no. 3, p. 774-788, https://doi.org/10.3969/j.issn.0253-4967.2019.03.015.
Fan X., Xu Q., Liu J., et al., 2019, Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China: Landslides, v. 16, no. 12, p. 2445-2457, https://doi.org/10.1007/s10346-019-01269-6. DOI: https://doi.org/10.1007/s10346-019-01269-6
Kuglitsch M. M., Cox J., Luterbacher J., et al., 2024, AI to the rescue: how to enhance disaster early warnings with tech tools: Nature, v. 634, no. 8032, p. 27-29, https://doi.org/10.1038/D41586-024-03149-Z. DOI: https://doi.org/10.1038/d41586-024-03149-z
Naidu S., Sajinkumar K. S., Oommen T., et al., 2018, Early warning system for shallow landslides using rainfall threshold and slope stability analysis: Geoscience Frontiers, v. 9, no. 6, p. 1871-1882, https://doi.org/10.1016/j.gsf.2017.10.008. DOI: https://doi.org/10.1016/j.gsf.2017.10.008
Rasheed Z., Aravamudan A., Sefidmazgi A. G., et al., 2022, Advancing flood warning procedures in ungauged basins with machine learning: Journal of Hydrology, v. 609, p. 127736. DOI: https://doi.org/10.1016/j.jhydrol.2022.127736
Li W., Tang C. and Chang M., 2013, Investigation and monitoring & warning sys tem design of debris flows in daseer valley in the area of wenchuan earthquake: Journal of Geological Hazards and Environment Preservation, v. no. 4, p. 95-102.
Feng Z., Li B., Zhao C., et al., 2016, Geological hazards monitoring and application in mountainous town of three gorges reservoir: Journal of Geomechanics, v. 22, no. 03, p. 685-694.
Ren Z., Sang Y., Yang M., et al., 2023, Progress of research on the methods for the early warning of mountain flash flood disasters: Progress in Geography, v. p. DOI: https://doi.org/10.18306/dlkxjz.2023.01.015
Qian H., Wang W. and Chen G., 2024, Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts: Journal of Hydrology, v. 664, p. 132101-132101. DOI: https://doi.org/10.1016/j.jhydrol.2024.132101
Zhao B., Chen E., Dai Q., et al., 2022, Study on prediction of regional rainfall-induced landslides based on hydro-meteorological threshold: Acta Geodaetica et Cartographica Sinica, v. 51, no. 10, p. 2216-2225.
Xu Q., Huang R. and Li X., 2004, Research progress in time forecast and prediction of landslides: Advance in Earth Sciences, v. 19, no. 3, p. 478-483.
Yang Z. and Chen J., 2004, Thoughts on the prediction or forecast of landslides: Journal of Engineering Geology, v. 12, no. 2, p. 118-123.
Fan X., Li J. and Sui D., 1988, Application of Space Remote Sensing Technology in Natural Disaster Research: Journal of Catastrophology, v. 4, p. 70-74.
Liu J., Yang J., Wei C., et al., 2004, Acquisition of earthquake damage information basedon remote sensing technology:history,current situation and trend: Journal of Natural Disasters, v. 13, no. 6, p. 46-52.
Zhang Y., 1990, State-of-the-art of application of remote sensing technique to disaster research in foreign countries: Journal of Catastrophology, v. no. 2, p. 61-65.
Iglseder H., Arens-Fischer W. and Wolfsberger W., 1995, Small satellite constellations for disaster detection and monitoring: Advances in Space Research v. 15, no. 11, p. 79-85, https://doi.org/10.1016/0273-1177(95)00077-R. DOI: https://doi.org/10.1016/0273-1177(95)00077-R
Foody G. M. and Arora M. K., 1997, An evaluation of some factors affecting the accuracy of classification by an artificial neural network: International Journal of Remote Sensing, v. 18, no. 4, p. 799-810. DOI: https://doi.org/10.1080/014311697218764
Li H. and Zheng J., 1995, A review of the application of artificial neural networks in disaster prediction: Earthquake information, v. 12, p. 7-12.
Gassner M. and Brabec B., 2002, Nearest neighbour models for local and regional avalanche forecasting: Natural Hazards and Earth System Sciences, v. 2, no. 3/4, p. 247-253. DOI: https://doi.org/10.5194/nhess-2-247-2002
Xu C., Dai F., Chen J., et al., 2009, Identification and analysis of secondary geological hazards triggered by a magnitude 8.0 Wenchuan Earthquake: National Remote Sensing Bulletin, v. 13, no. 4, p. 754-762, https://doi.org/10.3321/j.issn:1007-4619.2009.04.016.
Xue Z., Xu C. and Xu X., 2023, Application of ChatGPT in natural disaster prevention and reduction: Natural Hazards Research, v. 3, no. 3, p. 556-562. DOI: https://doi.org/10.1016/j.nhres.2023.07.005
Zhang W., Pradhan B., Stuyts B., et al., 2023, Application of artificial intelligence in geotechnical and geohazard investigations: Geological Journal, v. 58, no. 6, p. 2187-2194. DOI: https://doi.org/10.1002/gj.4779
Liu S., Huang P., Chen G., et al., 2020, Research on Big Data and Emergency Prevention of Disaster based on IOT and the Internet: Journal of Chengdu University of Information Technology, v. p.
Xu C., 2012, Detailed Inventory of Landslides Triggered by the 2008 Wenchuan Earthquake and Its Comparison with Other Earthquake Events in the World: Science & Technology Review, v. 30, no. 25, p. 18-26, https://doi.org/10.3981/j.issn.1000-7857.2012.25.001.
Ahmed B., Rahman M. S., Islam R., et al., 2018, Developing a dynamic Web-GIS based landslide early warning system for the Chittagong Metropolitan Area, Bangladesh: ISPRS International Journal of Geo-Information, v. 7, no. 12, p. 485. DOI: https://doi.org/10.3390/ijgi7120485
Wu X., Xu C., Xu X., et al., 2022, A Web-GIS hazards information system of the 2008 Wenchuan Earthquake in China: Natural Hazards Research, v. 2, no. 3, p. 210-217. DOI: https://doi.org/10.1016/j.nhres.2022.03.003
Yang Z., Qi W., Xu C., et al., 2024, Exploring deep learning for landslide mapping: A comprehensive review: China Geology, v. 7, no. 2, p. 330-350. DOI: https://doi.org/10.31035/cg2024032
Guo Y., 2025, Problems and countermeasures of safety production of flood and drought disaster prevention materials: Haihe Water Resources, v. 1, p. 56-58,63, https://doi.org/10.3969/j.issn.1004-7328.2025.01.014.
Ibrahim A., Salifu A.-H. and Peprah C., 2023, Does governance matter when disaster looms? Zooming into proactive institutional measures for flood risk management: International Journal of Disaster Risk Reduction, v. 97, no. 15, p. 104021, https://doi.org/10.1016/j.ijdrr.2023.104021. DOI: https://doi.org/10.1016/j.ijdrr.2023.104021
Marchezini V., Saito S. M., Londe L. R., et al., 2025, Implementation challenges of disaster risk management policies: The organizational capacities of municipal civil defense units: International Journal of Disaster Risk Reduction, v. 119, p. 105291, https://doi.org/10.1016/j.ijdrr.2025.105291. DOI: https://doi.org/10.1016/j.ijdrr.2025.105291
Xu X., O'Sullivan J. J., Abolfathi S., et al., 2025, Advances in understanding the challenges and opportunities of hybrid sea defence approaches for coastal resilience: Environmental Challenges, v. 19, p. 101130, https://doi.org/10.1016/j.envc.2025.101130. DOI: https://doi.org/10.1016/j.envc.2025.101130
Jing S., Shi L. and Liu J., 2024, Natural disaster emergency management theme heat and evolutionary patterns——An analysis based on artificial intelligence language model: Journal of China Emergency Management Science, v. 6, p. 34-51.
Liu J., Zhang Y., Xu S., et al., 2018, Top-Level Design Study for the Integrated Disaster Reduction Intelligent Service: Geomatics and Information Science of Wuhan University, v. 43, no. 12, p. 2250-2258, https://doi.org/10.13203/j.whugis20180309.
Zhang M., Jia J., Wang Y., et al., 2019, Construction of Geological Disaster Prevention and Control System Based on AI: Northwestern Geology, v. 52, no. 2, p. 103-116.
Dikshit A., Pradhan B., Matin S. S., et al., 2024, Artificial Intelligence: A new era for spatial modelling and interpreting climate-induced hazard assessment: Geoscience Frontiers, v. 15, p. 101815, https://doi.org/10.1016/j.gsf.2024.101815. DOI: https://doi.org/10.1016/j.gsf.2024.101815
Mondini A. C., Guzzetti F. and Melillo M., 2023, Deep learning forecast of rainfall-induced shallow landslides: Nature Communications, v. 14, no. 1, p. 2466, https://doi.org/10.1038/s41467-023-38135-y. DOI: https://doi.org/10.1038/s41467-023-38135-y
Yu J., 2021, Anti-seismic Performance Research Progress about Using High Performance Materials for Strengthening Masonry Building: Jiangsu Construction, v. S1, p. 70-74.
Zhu Y., Wang L., Chen L., et al., 2024 Multiple symmetric multi-link search and rescue robot based on a new movable structure Applied Science and Innovative Research, v. 8, no. 4, p. 133-137, https://doi.org/10.22158/ASIR.V8N4P133. DOI: https://doi.org/10.22158/asir.v8n4p133
Dowling C. A. and Santi P. M., 2014, Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011: Natural hazards, v. 71, p. 203-227. DOI: https://doi.org/10.1007/s11069-013-0907-4
Paprotny D., Sebastian A., Morales-Nápoles O., et al., 2018, Trends in flood losses in Europe over the past 150 years: Nature communications, v. 9, no. 1, p. 1-12. DOI: https://doi.org/10.1038/s41467-018-04253-1
Wyss M., Speiser M. and Tolis S., 2022, Earthquake fatalities and potency: Natural Hazards, v. p. 1-16. DOI: https://doi.org/10.1007/s11069-022-05627-x
Xu X., Wang Z., Xu C., et al., 2021, Natural Disaster Risk Analysis and Its Countermeasures of Major Urban Agglomerations in China: City and Disaster Reduction, v. p.
Xu C., 2018, Landslide seismology geology: A sub-discipline of environmental earth sciences: Journal of Engineering Geology, v. 26, no. 1, p. 207-222, https://doi.org/10.13544/j.cnki.jeg.2018.01.022.
Zhang P., Zhang Y., Wang Y., et al., 2024, Analysis of temporal-spatial patterns and impact factors of typhoon disaster losses in China from 1978 to 2020: Tropical Geography, v. 44, no. 6, p. 1047-1063, https://doi.org/10.13284/j.cnki.rddl.20230961.
Zhou Y., Peng T. and Shi R., 2019, Research progress on risk assessment of heavy rainfall and flood disasters in China: Torrential Rain and Disasters, v. 38, no. 5, p. 494-501.
Yimingbahai A., 2018, Report of the Law-Enforcement Inspection Team of the Standing Committee of the National People’s Congress on the Inspections of the Enforcement of the Law of the People’s Republic of China on Protecting Against and Mitigating Earthquake Disasters: Gazette of the Standing Committee of the National People's Congress of the People's Republic of China, v. 6, p. 996-1003.
Gao M., Zhou B. and Pan H., 2008, Damage Characteristics and Enlightenment of Disaster Prevention of "5.12" Wenchuan Earthquake: Technology for Earthquake Disaster Prevention, v. 3, no. 3, p. 205-219.
Wang D., 2023, Analysis and enlightenment of Türkiye M7.8 earthquake disaster: Disaster Reduction in China, v. 3, p. 20-23.
Gao H., Xu C., Xie C., et al., 2024, Landslides triggered by the July 2023 extreme rainstorm in the Haihe River Basin, China: Landslides, v. 21, p. 2885–2890, https://doi.org/10.1007/s10346-024-02322-9. DOI: https://doi.org/10.1007/s10346-024-02322-9
Xiao H., 2023, Response to "23 · 7" extremely heavy rainstorm Flood Disaster in Beijing and Its Main Enlightenment: Disaster Reduction in China, v. 23, p. 28-31.
Zander K. K., Nguyen D., Mirbabaie M., et al., 2023, Aware but not prepared: understanding situational awareness during the century flood in Germany in 2021: International Journal of Disaster Risk Reduction, v. 96, p. 103936, https://doi.org/10.1016/j.ijdrr.2023.103936. DOI: https://doi.org/10.1016/j.ijdrr.2023.103936
Zhang B., 2024, Fuzhou, Fujian: Response to super typhoon "Doksuri" and main implications: Disaster Reduction in China, v. 14, p. 42-45.
Liu Y., Liu C., Wen M., et al., 2015, Study of early warning models for regional geo-hazards in china: Journal of Engineering Geology, v. 23, no. 4, p. 738-746.
Keefer D. K., 1984, Rock avalanches caused by earthquakes: source characteristics: Science, v. 223, no. 4642, p. 1288-1290. DOI: https://doi.org/10.1126/science.223.4642.1288
Xu C., Dai F. and Xu X., 2010, Wenchuan Earthquake-induced Landslides:an Overview: Geological Review, v. 56, no. 6, p. 860-874, https://doi.org/10.16509/j.georeview.2010.06.017.
Mercurio C., Calderón-Cucunuba L. P., Argueta-Platero A. A., et al., 2023, Predicting earthquake-induced landslides by using a stochastic modeling approach: A case study of the 2001 El Salvador coseismic landslides: ISPRS International Journal of Geo-Information, v. 12, no. 4, p. https://doi.org/10.3390/ijgi12040178. DOI: https://doi.org/10.3390/ijgi12040178
Dai L., Fan X., Wang X., et al., 2023, Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China: Landslides, v. p. https://doi.org/10.1007/s10346-023-02061-3. DOI: https://doi.org/10.1007/s10346-023-02061-3
Li T., Ma J., Huang Y., et al., 2025, Disaster analysis and lessons learned from the July 22, 2024, Ethiopian landslide: Earthquake Research Advances, v. 2025, p. 100358, https://doi.org/10.1016/j.eqrea.2025.100358. DOI: https://doi.org/10.1016/j.eqrea.2025.100358
Xie Y., 2010, Introspection on Planning of Disaster Prevention and Mitigation and Planning of Post-disaster Reconstruction——Something Learned from the Zhouqu Debris-Flow Disaster: Journal of Catastrophology, v. B10, p. 16-19.
Liu C., Shen W. and Huang S., 2022, Some Viewpoints on Strategies in Risk Reduction of Geological Disasters in China: Journal of Catastrophology, v. 37, no. 3, p. 1-4+11.