Intelligent decision support system for post‑disaster damage assessment: Integrating modified‑topsis and neural networks for enhanced decision‑making performance

Fenti Yulia Kristanti; Agung Tegu Wibowo Almais; Muhammad Faisal

doi:10.64268/decision.v1i1.2

Authors

Fenti Yulia Kristanti Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia Author
Agung Tegu Wibowo Almais Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia Author
Muhammad Faisal Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia Author

DOI:

https://doi.org/10.64268/decision.v1i1.2

Keywords:

Intelligent Decision, Modified-TOPSIS, Neural Networks, Post Disaster, Multi Criteria

Abstract

Purpose: This study aims to develop an advanced Dynamic Intelligent Decision Support System (IDSS) for determining the damage level of sectors affected by natural disasters.

Method: The research combines the Modified-TOPSIS method, a Multi-Criteria Decision-Making (MCDM) approach, with Neural Networks to improve post-disaster damage assessment. The method applies Pairwise Comparison for weighting criteria, and the Neural Network learns from historical data to enhance the accuracy of decisions. The system was tested with data from Blitar, East Java, and evaluated for accuracy using metrics such as precision, recall, and F-measure.

Findings: The findings indicate that the Modified-TOPSIS method achieves 75% accuracy, while incorporating Neural Networks improves accuracy to 81%. The integration of Neural Networks provides more reliable and objective results compared to the Modified-TOPSIS method alone, particularly in managing complex data.

Significance: This study contributes significantly to disaster management by offering a reliable, data-driven tool for assessing damage in affected sectors. The integration of AI with classical MCDM methods represents a major advancement in disaster recovery, providing more timely and efficient decision-making to aid rehabilitation efforts.

References

Aldeeb, O., & Qasem, A. (2022). Decision support system (DSS) for facilities rehabilitation and management (part 1): Development of integrated AHP-MAUT performance assessment model (PAM). Facilities, 40(13-14), 845-861. https://doi.org/10.1108/F-04-2022-0058

Braik, A. M., & Koliou, M. (2025). Post-tornado automated building damage evaluation and recovery prediction by integrating remote sensing, deep learning, and restoration models. Sustainable Cities and Society, 123, 106286. https://doi.org/10.1016/j.scs.2025.106286

Chaube, S., Pnt, S., Kumar, A., Uniyal, S., Singh, M., Kotecha, K., & Kumar, A. (2024). An Overview of Multi-Criteria Decision Analysis and the Applications of AHP and TOPSIS Methods. International Journal of Mathematical, Engineering and Management Sciences, 9, 581-615. https://doi.org/10.33889/IJMEMS.2024.9.3.030

Fonseca, Â., Ferreira, A., Ribeiro, L., Moreira, S., & Duque, C. (2024). Embracing the future-is artificial intelligence already better? A comparative study of artificial intelligence performance in diagnostic accuracy and decision-making. European Journal of Neurology, 31(4), e16195. https://doi.org/10.1111/ene.16195

Goel, A., Goel, A. K., & Kumar, A. (2023). The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research, 31(3), 275–285. https://doi.org/10.1007/s41324-022-00494-x

Jana, S., Majumder, R., Menon, P. P., & Ghose, D. (2022). Decision Support System (DSS) for Hierarchical Allocation of Resources and Tasks for Disaster Management. Operations Research Forum, 3(3), 37. https://doi.org/10.1007/s43069-022-00148-6

Jia, J., & Ye, W. (2023). Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities. Remote Sensing, 15(16), 4098. https://doi.org/10.3390/rs15164098

Jung, D., Tuan, V. T., Tran, D. Q., Park, M., & Park, S. (2020). Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management. Applied Sciences, 10(2). https://doi.org/10.3390/app10020666

Khan, S. M., Shafi, I., Butt, W. H., Diez, I. de la T., Flores, M. A. L., Galán, J. C., & Ashraf, I. (2023). A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions. Land, 12(8). https://doi.org/10.3390/land12081514

Kostopoulos, G., Davrazos, G., & Kotsiantis, S. (2024). Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review. Electronics, 13(14). https://doi.org/10.3390/electronics13142842

Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. L. (2022). Machine learning in disaster management: recent developments in methods and applications. Machine Learning and Knowledge Extraction, 4(2).

https://doi.org/10.3390/make4020020

Lozano, J.-M., & Tien, I. (2023). Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems. International Journal of Disaster Risk Reduction, 94, 103819. https://doi.org/10.1016/j.ijdrr.2023.103819

Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025). Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human. Neural Processing Letters, 57(1), 16. https://doi.org/10.1007/s11063-025-11732-2

Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies-Part 2: Recommendations and Best Practices. Remote Sensing, 13(13). https://doi.org/10.3390/rs13132591

Mohammadnazari, Z., Mamoudan, M. M., Alipour-Vaezi, M., Aghsami, A., Jolai, F., & Yazdani, M. (2022). Prioritizing Post-Disaster Reconstruction Projects Using an Integrated Multi-Criteria Decision-Making Approach: A Case Study. Buildings, 12(2). https://doi.org/10.3390/buildings12020136

Mojtahedi, M., Sunindijo, R. Y., Lestari, F., Suparni, & Wijaya, O. (2021). Developing Hospital Emergency and Disaster Management Index Using TOPSIS Method. Sustainability, 13(9). https://doi.org/10.3390/su13095213

Mushtaha, A. W., Alaloul, W. S., Baarimah, A. O., Musarat, M. A., Alzubi, K. M., & Khan, A. M. (2025). A decision-making framework for prioritizing reconstruction projects in post-disaster recovery. Results in Engineering, 25, 103693. https://doi.org/10.1016/j.rineng.2024.103693

Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2025). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Management Decision, 63(10), 3501-3532. https://doi.org/10.1108/MD-10-2023-1946

Potur, E. A., Aktas, A., & Kabak, M. (2025). A Bibliometric Analysis of Multi-Criteria Decision-Making Techniques in Disaster Management and Transportation in Emergencies: Towards Sustainable Solutions. Sustainability, 17(6). https://doi.org/10.3390/su17062644

Ravi, M., Negi, A., Bommi, N. S., & Rouf, N. (2025). Evolution of AI-Driven Decision Making with Decision Support Systems, Expert Systems, Recommender Systems, and XAI. IETE Technical Review, 42(4), 428-465. https://doi.org/10.1080/02564602.2025.2512086

Rehan, H. (2022). Enhancing Disaster Response Systems: Predicting and Mitigating the Impact of Natural Disasters Using AI. 2, 501-540.

Rezvani, S. M., Falcão, M. J., Komljenovic, D., & Almeida, N. M. de. (2023). A Systematic Literature Review on Urban Resilience Enabled with Asset and Disaster Risk Management Approaches and GIS-Based Decision Support Tools. Applied Sciences, 13(4). https://doi.org/10.3390/app13042223

Rithani, M., Kumar, R. P., & Doss, S. (2023). A review on big data based on deep neural network approaches. Artificial Intelligence Review, 56(12), 14765–14801. https://doi.org/10.1007/s10462-023-10512-5

Rouhanizadeh, B., Kermanshachi, S., & Nipa, T. J. (2020). Exploratory analysis of barriers to effective post-disaster recovery. International Journal of Disaster Risk Reduction, 50, 101735. https://doi.org/10.1016/j.ijdrr.2020.101735

Shafian, S. A., & Hu, D. (2024). Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment. Buildings, 14(8). https://doi.org/10.3390/buildings14082344

Taherdoost, H. (2023). Deep Learning and Neural Networks: Decision-Making Implications. Symmetry, 15(9). https://doi.org/10.3390/sym15091723

Tan, L., Guo, J., Mohanarajah, S., & Zhou, K. (2021). Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Natural Hazards, 107(3), 2389-2417. https://doi.org/10.1007/s11069-020-04429-3

Wang, K., Wang, Z., Deng, J., Feng, Y., & Li, Q. (2022). Study on the Evaluation of Emergency Management Capacity of Resilient Communities by the AHP-TOPSIS Method. International Journal of Environmental Research and Public Health, 19(23). https://doi.org/10.3390/ijerph192316201

Zabihi, O., Siamaki, M., Gheibi, M., Akrami, M., & Hajiaghaei-Keshteli, M. (2023). 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, 84, 103470. https://doi.org/10.1016/j.ijdrr.2022.103470

Zhang, L., & Pan, Y. (2022). Information fusion for automated post-disaster building damage evaluation using deep neural network. Sustainable Cities and Society, 77, 103574. https://doi.org/10.1016/j.scs.2021.103574