Spatial Vulnerability Index Modeling for Climate Change Risk Assessment in Indonesia
DOI:
https://doi.org/10.26877/asset.v8i3.2435Keywords:
Climate change, climate vulnerability, decision support model, fuzzy logic, risk mappingAbstract
This study develops a Fuzzy Logic-based Intelligent Decision Support Model (IDSM) to map climate change vulnerability across 34 Indonesian provinces, using 20 variables grouped into four parameters: greenhouse gases, geological factors, anthropological factors, and weather conditions. Data from 2015–2020 were sourced from the Global Atmosphere Watch, LAPAN, BASINS-CAT, BPS, and the DigComp 2.0 framework at the provincial level. The methodology involved data normalization, trend analysis via linear regression, relative value calculation using Euclidean distance, and a two-stage aggregation through a fuzzy inference system to produce a Vulnerability Score. Results indicate Jakarta as the most vulnerable (0.6145), Bali as the least vulnerable (0.498), and West Kalimantan (0.502), Maluku (0.5215), and Papua (0.500) as moderately vulnerable. These variations stem from differing environmental, social, and economic conditions, highlighting the need for location-specific adaptation and mitigation policies. The model offers a valuable tool for prioritizing climate action interventions.
References
[1] Jiskani IM, Cai Q, Zhou W, Lu X, Shah SAA. An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining. Expert Systems with Applications 2022;188:116062. https://doi.org/https://doi.org/10.1016/j.eswa.2021.116062
[2] Imamguluyev R, Abbasov I, Sadigov R, Imanova T, Sharifli I, Bagirov G, et al. Smart Decision-Making in the Green Economy: A Fuzzy Logic Approach BT - Computing and Machine Learning. In: Bansal JC, Borah S, Hussain S, Salhi S, editors., Singapore: Springer Nature Singapore; 2024, p. 153–64. https://doi.org/10.1007/978-981-97-7571-2_13
[3] Shelash S, Vasudevan A, Alqahtani MM, Sun X, Ali I. Leveraging Fuzzy Logic for Resilient Agricultural Supply Chains: Risk Mitigation and Decision-Making in Jordan. Research on World Agricultural Economy 2025;6:188–203. https://doi.org/10.36956/rwae.v6i3.1602
[4] Gunathilaka MDKL, Harshana WTS. Evaluation of Urban Heat Island ( UHI ) Spatial Change in Freshwater Lakes with Hot Spot Analysis ( GI Statistics ). International Journal of Environment, Engineering & Education 2021;3:48–58. https://doi.org/10.55151/ijeedu.v3i2.54
[5] Abbass K, Qasim MZ, Song H, Murshed M, Mahmood H, Younis I. A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research 2022;29:42539–59. https://doi.org/10.1007/s11356-022-19718-6
[6] Rawat A, Kumar D, Khati BS. A review on climate change impacts, models, and its consequences on different sectors: a systematic approach. Journal of Water and Climate Change 2023;15:104–26. https://doi.org/10.2166/wcc.2023.536
[7] Seong M-G, Min S-K, Kim Y-H, Zhang X, Sun Y. Anthropogenic greenhouse gas and aerosol contributions to extreme temperature changes during 1951–2015. Journal of Climate 2021;34:857–70. https://doi.org/10.1175/JCLI-D-19-1023.1
[8] Filonchyk M, Peterson MP, Zhang L, Hurynovich V, He Y. Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O. Science of The Total Environment 2024;935:173359. https://doi.org/https://doi.org/10.1016/j.scitotenv.2024.173359
[9] Irawan Y, Sabna E, Azim AF, Wahyuni R, Belarbi N, Josephine MM. Automatic Chili Plant Watering Based On Internet Of Things (IoT). Journal of Applied Engineering and Technological Science (JAETS) 2022;3:77–83. https://journal.yrpipku.com/index.php/jaets/article/view/532/412
[10] Mondal S, Lee M-A. Impact of Global Warming on Fisheries BT - Food Security, Nutrition and Sustainability Through Aquaculture Technologies. In: Sundaray JK, Rather MA, Ahmad I, Amin A, editors., Cham: Springer Nature Switzerland; 2025, p. 227–53. https://doi.org/10.1007/978-3-031-75830-0_12
[11] Robinson WA. Climate change and extreme weather: A review focusing on the continental United States. Journal of the Air & Waste Management Association 2021;71:1186–209. https://doi.org/10.1080/10962247.2021.1942319
[12] Harvey JA, Tougeron K, Gols R, Heinen R, Abarca M, Abram PK, et al. Scientists’ warning on climate change and insects. Ecological Monographs 2023;93:e1553. https://doi.org/https://doi.org/10.1002/ecm.1553
[13] Roest AH, Weitkamp G, van den Brink M, Boogaard F. Mapping spatial opportunities for urban climate adaptation measures in public and private spaces using a GIS-based Decision Support Model. Sustainable Cities and Society 2023;96:104651. https://doi.org/https://doi.org/10.1016/j.scs.2023.104651
[14] Maragno D, Dall’Omo CF, Pozzer G, Musco F. Multi-Risk Climate Mapping for the Adaptation of the Venice Metropolitan Area. Sustainability 2021;13:1334. https://doi.org/10.3390/su13031334
[15] Tholo HM, Kadewa W, Chisenga C, Gondwe S, Zuza E, Mwase W, et al. Web-based spatial decision support system for optimum route to forest fires: A case of Viphya plantations. Trees, Forests and People 2025;19:100740. https://doi.org/https://doi.org/10.1016/j.tfp.2024.100740
[16] Cerreta M, Panaro S, Poli G. A Spatial Decision Support System for Multifunctional Landscape Assessment: A Transformative Resilience Perspective for Vulnerable Inland Areas. Sustainability 2021;13:2748. https://doi.org/10.3390/su13052748
[17] Truong PM, Le NH, Hoang THD, Nguyen TKT, Nguyen TD, Kieu TK, et al. Climate change vulnerability assessment using GIS and fuzzy AHP on an indicator-based approach. International Journal of Geoinformatics 2023;19:39–53. https://doi.org/10.52939/ijg.v19i2.2565
[18] Javanshiri Z, Pakdaman M. 8 - Artificial intelligence and decision making in climate change studies: A review. In: Ahmadian A, Salahshour S, Balas VE, Baleanu DBT-U in CI-BDM, editors. Advanced Studies in Complex Systems, Academic Press; 2025, p. 109–18. https://doi.org/https://doi.org/10.1016/B978-0-443-21475-2.00011-4
[19] Sahil K, Mehta P, Kumar Bhardwaj S, Dhaliwal LK. Chapter 20 - Development of mitigation strategies for the climate change using artificial intelligence to attain sustainability. In: Srivastav A, Dubey A, Kumar A, Kumar Narang S, Ali Khan MBT-VT for CC with ML and AI, editors., Elsevier; 2023, p. 421–48. https://doi.org/https://doi.org/10.1016/B978-0-323-99714-0.00021-2
[20] Zabihi O, Siamaki M, Gheibi M, Akrami M, Hajiaghaei-Keshteli M. A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. International Journal of Disaster Risk Reduction 2023;84:103470. https://doi.org/https://doi.org/10.1016/j.ijdrr.2022.103470
[21] Nyangon J. Climate-proofing critical energy infrastructure: Smart grids, artificial intelligence, and machine learning for power system resilience against extreme weather events. Journal of Infrastructure Systems 2024;30:3124001.
[22] Safari A, Daneshvar M, Anvari-Moghaddam A. Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management. Applied Sciences 2024;14:11112. https://doi.org/10.3390/app142311112
[23] Kikon A, Deka PC. Artificial intelligence application in drought assessment, monitoring and forecasting: a review. Stochastic Environmental Research and Risk Assessment 2022;36:1197–214. https://doi.org/10.1007/s00477-021-02129-3
[24] Vuorikari R, Punie Y, Gomez SC, Van Den Brande G. DigComp 2.0: The digital competence framework for citizens. Update phase 1: The conceptual reference model. Joint Research Centre; 2016.
[25] Law BE, Hudiburg TW, Berner LT, Kent JJ, Buotte PC, Harmon ME. Land use strategies to mitigate climate change in carbon dense temperate forests. Proceedings of the National Academy of Sciences 2018;115:3663–8. https://doi.org/10.1073/pnas.1720064115
[26] Taghizadeh M, Hadi-Vencheh A, Varnamkhasti MJ, Jamshidi A. Harnessing Interval Fuzzy Numbers: A Novel Approach to Multi-Criteria Decision-Making Models. International Journal of Mathematical Modelling & Computations 2025;15:161–79. https://doi.org/10.71932/ijm.2025.1206327
[27] Sakthivel AD, Augustin F, Deivanayagam Pillai N. A comprehensive intuitionistic fuzzy cognitive map with multi-criteria decision-making system for post-COVID challenges. Iran Journal of Computer Science 2025;8:1073–1095. https://doi.org/10.1007/s42044-025-00260-w
[28] Balogh W, Kurino T. The World Meteorological Organization and Space-Based Observations for Weather, Climate, Water and Related Environmental Services BT - Space Capacity Building in the XXI Century. In: Ferretti S, editor., Cham: Springer International Publishing; 2020, p. 223–32. https://doi.org/10.1007/978-3-030-21938-3_20
[29] Vuorikari R, Punie Y, Gomez SC, Van Den Brande G. DigComp 2.0: The digital competence framework for citizens. Update phase 1: The conceptual reference model. Joint Research Centre; 2016. https://doi.org/10.1080/02827581.2016.1212088
[30] Czimber K, Gálos B. A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research 2016;31:664–73. https://doi.org/10.1080/02827581.2016.1212088
[31] Kazak JK. The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland). Sustainability 2018;10. https://doi.org/10.3390/su10041083
[32] Bin L, Shahzad M, Khan H, Bashir MM, Ullah A, Siddique M. Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology. Sustainability 2023;15:13874. https://doi.org/10.3390/su151813874
[33] Hanoon SK, Abdullah AF, Shafri HZM, Wayayok A. Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq. Earth 2022;3:699–732. https://doi.org/10.3390/earth3020040
[34] Katushabe C, Kumaran S, Masabo E. Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa. Applied System Innovation 2021;4:44. https://doi.org/10.3390/asi4030044
[35] Czimber K, Gálos B. A new decision support system to analyse the impacts of climate change on the Hungarian forestry and agricultural sectors. Scandinavian Journal of Forest Research 2016;31:664–73. https://doi.org/10.1080/02827581.2016.1212088
[36] Kazak JK. The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland). Sustainability 2018;10. https://doi.org/10.3390/su10041083
[37] Hanoon SK, Abdullah AF, Shafri HZM, Wayayok A. Comprehensive Vulnerability Assessment of Urban Areas Using an Integration of Fuzzy Logic Functions: Case Study of Nasiriyah City in South Iraq. Earth 2022;3:699–732. https://doi.org/10.3390/earth3020040
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