Abstract
The rising frequency of extreme natural disasters under global climate change presents significant uncertainty and safety challenges for emergency logistics facility planning, particularly in mountainous regions. Addressing the limitations of traditional location studies that often overlook terrain complexity and disaster risks, this study proposes an integrated framework for mountainous emergency logistics site selection, combining Geographic Information Systems (GIS) spatial analysis with Multi-Criteria Decision-Making (MCDM) methods. Using 88 counties and districts in Guizhou Province as a case study, the framework incorporates four criterion-level dimensions and eight indicator-level factors, and employs GIS spatial analysis alongside a hybrid BWM–EWM weighting scheme and an improved TOPSIS evaluation to generate a suitability map. Sensitivity analysis confirms the robustness of the model. The results reveal an east-strong–west-weak spatial pattern of suitability, with population density and transportation accessibility as dominant factors, terrain imposing fundamental constraints, and natural disaster risk providing critical differentiation for decision-making.
Similar content being viewed by others
Data availability
Original data may be obtained from the corresponding author upon reasonable request.
References
https://www.statista.com/statistics/510959/number-of-natural-disasters-events-globally/
https://www.statista.com/statistics/510952/number-of-deaths-from-natural-disasters-globally/
Zhong, Q., Chen, S., Wang, L. & Shan, Y. Back analysis of breaching process of Baige landslide dam. Landslides 17(7), 1681–1692. https://doi.org/10.1007/s10346-020-01398-3 (2020).
Zhou, J., Dou, J., Wang, X., Fu, X., Ran, Q., & Pradhan, B. Water-Related Natural Disasters in Mountainous Area, volume II. In Frontiers Media SA, (2024).
Russo, F. & Rindone, C. Data envelopment analysis (DEA) for evacuation planning (Algarve, 2010).
Kemball-Cook, D. & Stephenson, R. Lessons in logistics from Somalia. Disasters 8(1), 57–66. https://doi.org/10.1111/j.1467-7717.1984.tb00853.x (1984).
Santangelo, N., Forte, G., De Falco, M., Chirico, G. B. & Santo, A. New insights on rainfall triggering flow-like landslides and flash floods in Campania (Southern Italy). Landslides 18(8), 2923–2933 (2021).
Li, P. & Fan, X. The application of the improved jellyfish search algorithm in a site selection model of an emergency logistics distribution center considering time satisfaction. Biomimetics 8, 349 (2023).
Zhang, R., Li, J. & Shang, Y. Multi-level site selection of mobile emergency logistics considering safety stocks. Appl. Sci. 13, 11245 (2023).
Long, S., Zhang, D., Liang, Y., Li, S. & Chen, W. Robust optimization of the multi-objective multi-period location-routing problem for epidemic logistics system with uncertain demand. IEEE Access 9, 151912–151930. https://doi.org/10.1109/ACCESS.2021.3125746 (2021).
Wang, H. & Ma, X. Research on multiobjective location of urban emergency logistics under major emergencies. Math. Probl. Eng. 2021, 1–12. https://doi.org/10.1155/2021/5577797 (2021).
Gutjahr, W. J. & Dzubur, N. Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transp. Res. Part E Logist. Transp. Rev. 85, 1–22 (2016).
Wang, Z., Leng, L., Ding, J. & Zhao, Y. Study on location-allocation problem and algorithm for emergency supplies considering timeliness and fairness. Comput. Ind. Eng. 177, 109078 (2023).
Xu, F., Ma, Y., Liu, C. & Ji, Y. Emergency logistics facilities location dual-objective modeling in uncertain environments. Sustainability https://doi.org/10.3390/su16041361 (2024).
Liu, Y., Wang, M. & Wang, Y. Location decision of emergency medical supply distribution centers under uncertain environment. Int. J. Fuzzy Syst. 26(5), 1567–1603 (2024).
Alidadi, G., Arefi Khorrami, Z. & Nekooie, M. A. Multi-criteria site selection for emergency operations centers in Tehran province: An integrated GIS-AHP approach. GeoJournal 90(4), 180 (2025).
Qi, C., Fang, J. & Sun, L. Implementation of emergency logistics distribution decision support system based on GIS. Clust. Comput. 22(S4), 8859–8867. https://doi.org/10.1007/s10586-018-1983-8 (2018).
Feng, Z. et al. Emergency logistics centers site selection by multi-criteria decision-making and GIS. Int. J. Disaster Risk Reduct. 96, 103921 (2023).
Russo, F. & Rindone, C. Methods for risk reduction: Training and exercises to pursue the planned evacuation. Sustainability 16, 1474 (2024).
Tan, H. et al. Characterization of extreme rainfall changes and response to temperature changes in Guizhou Province, China. Sci. Rep. https://doi.org/10.1038/s41598-024-71662-2 (2024).
Kun, C., & Xun, W. Research on the Consideration of Disaster Emergency Logistics Facility Location Problem. In Logistics Sci-tech. (2010).
Wang, J., Zhu, X., & Jiang, J. Emergency Logistics Center Location Selection Based on Hybrid Multinattribute. Proceedings of the 2016 International Forum on Management, Education and Information Technology Application. 2016 International Forum on Management, Education and Information Technology Application. https://doi.org/10.2991/ifmeita-16.2016.73 (2016).
Rezaei, J. Best-worst multi-criteria decision-making method. Omega 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009 (2015).
Ratandhara, H. M. & Kumar, M. An analytical framework for the multiplicative best‐worst method. J. Multi-Criteria Decis. Anal. https://doi.org/10.1002/mcda.1840 (2024).
Zhu, Y., Tian, D. & Yan, F. Effectiveness of entropy weight method in decision-making. Math. Probl. Eng. 2020, 1–5. https://doi.org/10.1155/2020/3564835 (2020).
Erkhembaatar, N. Evaluation of indicators for ICT development index using an integrated entropy weighting method. ICT Focus 2(1), 1–13. https://doi.org/10.58873/sict.v2i1.43 (2023).
Wen Tang, Ai-Zu Chen, Dong-Mei Li, & Ying Yang. The application of combination weighting approach in multiple attribute decision making. 2009 International Conference on Machine Learning and Cybernetics, 2724–2728. (2009).
Ai, L., Liu, S., Ma, L., & Huang, K. A Multi-attribute Decision Making Method Based on Combination of Subjective and Objective Weighting. 2019 5th International Conference on Control, Automation and Robotics (ICCAR), 576–580. https://doi.org/10.1109/iccar.2019.8813490 (2019).
Madanchian, M., & Taherdoost, H. A comprehensive guide to the TOPSIS method for multi-criteria decision making. Sustain. Soc. Devel., 1(1). https://doi.org/10.54517/ssd.v1i1.2220 (2023).
Li, H., Yazdi, M., Huang, C.-G. & Peng, W. A reliable probabilistic risk-based decision-making method: Bayesian technique for order of preference by similarity to ideal solution (B-TOPSIS). Soft Comput. 26(22), 12137–12153. https://doi.org/10.1007/s00500-022-07462-5 (2022).
Wang, F. et al. The Wuxie debris flows triggered by a record-breaking rainstorm on 10 June 2021 in Zhuji City, Zhejiang Province, China. Landslides 19(8), 1913–1934. https://doi.org/10.1007/s10346-022-01903-w (2022).
Yin, H., Xiang, Y., Chen, Z., Zhang, W. & Ao, Y. Heat vulnerability assessment and analysis of driving mechanisms in a megacity based on local climate zones: A street-level case study of Chengdu. Sustain. Cities Soc. 134, 106965 (2025).
Sarıkaya, M. Ş, Yanalak, M. & Karaman, H. Site selection of natural gas emergency response team centers in Istanbul Metropolitan Area based on GIS and FAHP. ISPRS Int. J. Geo-Inf. 11(11), 571 (2022).
Meng, L., Wang, X., He, J., Han, C. & Hu, S. A two-stage chance constrained stochastic programming model for emergency supply distribution considering dynamic uncertainty. Transp. Res. E Logist. Transp. Rev. 179, 103296 (2023).
Geng, J. An Overview of Emergency Logistics Routing and Location: Models and Future Research Directions. SHS Web of Conferences, 181, 03005. https://doi.org/10.1051/shsconf/202418103005 (2024).
Li, F. et al. Evaluating ecological vulnerability and its driving mechanisms in the Dongting Lake Region from a multi-method integrated perspective: Based on Geodetector and explainable machine learning. Land 14(7), 1435 (2025).
Funding
This study was supported by the Research Centre for Social Development and Social Risk Control, a key research base for philosophical and social sciences in Sichuan Province,SR23A05.
Author information
Authors and Affiliations
Contributions
Lin: conceptualisation, methodology, visualisation, writing- first draft, writing-review &editing; Xiang: Project administration, writing-review& editing, resources; Yin: Project administration, writing-review&editing, resources; Chen：Project administration, writing-review &editing, access to funding.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Lin, Y., Xiang, Y., Yin, H. et al. Selection of emergency logistics facility locations considering major natural disasters in mountainous cities based on GIS-MCDM. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43065-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-43065-y
