Flood Vulnerability Mapping in Cepu Subdistrict Using MamdaniFuzzy Inference System for Disaster Risk Reduction

Authors

  • Joko Handoyo Universitas Ahmad Dahlan, Yogyakarta, Indonesia
  • Anton Yudhana Universitas Ahmad Dahlan, Yogyakarta, Indonesia
  • Sunardi Sunardi Universitas Ahmad Dahlan, Yogyakarta, Indonesia

DOI:

https://doi.org/10.30812/matrik.v25i1.5390

Keywords:

Cepu Subdistrict, Fuzzy Inference System, Flood Vulnerability, Mamdani

Abstract

Floods pose a persistent and serious threat to Cepu Subdistrict, frequently causing significant economic loss, resident displacement, and damage to critical infrastructure. In response to this issue, and aligned with the National Disaster Management Agency's (BNPB) efforts to enhance landscape monitoring, a comprehensive analytical study was conducted. The purpose of this research was to assess and map the flood vulnerability levels across 17 villages in Cepu Subdistrict, categorizing them to facilitate more effective disaster response planning and resource allocation. The research method uses the Mamdani Fuzzy Inference System, an advanced computational approach adept at handling the non-linear relationships between environmental variables. This system allowed for a detailed analysis of the complex interactions among key flood-influencing factors, including rainfall intensity, watershed area, elevation, slope, and population density. The results of the quantitative research obtained from 17 villages in the Cepu Subdistrict show that Ngelo Village has the highest score of 65.16, categorized as a "high" risk level. In contrast, most other villages, such as Ngroto, Karangboyo, and Cabean, fell into the "medium" risk category with varying scores between 55.0 and 63.93. The model's accuracy was validated by evaluation metrics, with a Mean Absolute Error (MAE) of 8.67 and a Root Mean Squared Error (RMSE) of 10.29, indicating satisfactory predictive performance. The conclusion of this study emphasizes the urgent need for comprehensive and adaptive mitigation strategies, including early warning systems and community preparedness programs, to protect Cepu Subdistrict from future flood threats.

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References

[1] F. I. W. Rohmat, Z. Sa’adi, I. Stamataki, A. A. Kuntoro, M. Farid, and R. Suwarman, “Flood modeling and

baseline study in urban and high population environment: A case study of Majalaya, Indonesia,” Urban Climate,

vol. 46, p. 101332, dec 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2212095522002504https:

//linkinghub.elsevier.com/retrieve/pii/S2212095522002504

[2] S. Komsiyah, M. R. Ardyanti, and I. A. Iswanto, “Flood-Prone Susceptibility Analysis In Garut Using Fuzzy Inference System

Mamdani Method,” Procedia Computer Science, vol. 227, pp. 912–921, 2023. [Online]. Available: https://www.sciencedirect.

com/science/article/pii/S1877050923017659https://linkinghub.elsevier.com/retrieve/pii/S1877050923017659

[3] G. Wee, L.-C. Chang, F.-J. Chang, and M. Z. Mat Amin, “A flood Impact-Based forecasting system by fuzzy inference

techniques,” Journal of Hydrology, vol. 625, p. 130117, oct 2023. [Online]. Available: https://www.sciencedirect.com/science/

article/pii/S0022169423010594https://linkinghub.elsevier.com/retrieve/pii/S0022169423010594

[4] B. A. Kindhi, M. I. Triana, U. L. Yuhana, S. Damarnegara, F. Istiqomah, and M. H. Imaaduddiin, “Flood Identification

with Fuzzy Logic Based on Rainfall and Weather for Smart City Implementation,” in 2022 IEEE International

Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, nov 2022, pp. 67–72. [Online]. Available:

https://ieeexplore.ieee.org/document/9994512/

[5] Y. S. Jaya, L. Pramudita, D. Syahfitri, S.Wahyuni, Y. Triandriana, and B. D. A. Prayanti, “Implementation of the fuzzy mamdani

method in analyzing the level of flood vulnerability in Pangkalpinang city,” IOP Conference Series: Earth and Environmental

Science, vol. 1419, no. 1, p. 012078, dec 2024. [Online]. Available: https://dx.doi.org/10.1088/1755-1315/1419/1/012078https:

//iopscience.iop.org/article/10.1088/1755-1315/1419/1/012078

[6] R. Tabbussum and A. Q. Dar, “Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy

logic, and adaptive neuro-fuzzy inference system for flood prediction,” Environmental Science and Pollution Research,

vol. 28, no. 20, pp. 25 265–25 282, may 2021. [Online]. Available: https://doi.org/10.1007/s11356-021-12410-1https:

//link.springer.com/10.1007/s11356-021-12410-1

[7] K. Seeboruth, L. Z. Wen, V. A. Hameed, T. Y. Ling, K. P. Rajadorai, and M. E. Rana, “Fuzzy Logic Approach to Predicting

Rainfall Patterns,” in 2023 IEEE 21st Student Conference on Research and Development (SCOReD). IEEE, dec 2023, pp.

432–436. [Online]. Available: https://ieeexplore.ieee.org/document/10563899/

[8] I. K. Dulaimi, “Integrating Fuzzy Decision-Making and Artificial Intelligence in Crisis and

Disaster Management,” Scientific Research Journal of Engineering and Computer Sciences,

vol. 4, no. 2, pp. 1–10, 2024. [Online]. Available: https://www.iarconsortium.org/srjecs/53/2831/

-integrating-fuzzy-decision-making-and-artificial-intelligence-in-crisis-and-disaster-management--4648/

[9] Y. Wang, P. Zhang, Y. Xie, L. Chen, and Y. Li, “Toward explainable flood risk prediction: Integrating a novel hybrid machine

learning model,” Sustainable Cities and Society, vol. 120, p. 106140, feb 2025. [Online]. Available: https://www.sciencedirect.

com/science/article/pii/S2210670725000186https://linkinghub.elsevier.com/retrieve/pii/S2210670725000186

[10] S. Kambalimath and P. C. Deka, “A basic review of fuzzy logic applications in hydrology and water resources,” Applied

Water Science, vol. 10, no. 8, p. 191, aug 2020. [Online]. Available: https://doi.org/10.1007/s13201-020-01276-2https:

//link.springer.com/10.1007/s13201-020-01276-2

[11] A. Zalnezhad, A. Rahman, N. Nasiri, K. Haddad, M. M. Rahman, M. Vafakhah, B. Samali, and F. Ahamed, “Artificial

Intelligence-Based Regional Flood Frequency Analysis Methods: A Scoping Review,” Water, vol. 14, no. 17, p. 2677, aug

2022. [Online]. Available: https://dx.doi.org/10.1088/1755-1315/1419/1/012078https://www.mdpi.com/2073-4441/14/17/2677

[12] A. P. Sadanna, D. L. Setyowati, and E. Suharini, “Community vulnerability and resilience to flood disaster in Losari District,

Brebes Regency,” IOP Conference Series: Earth and Environmental Science, vol. 1314, no. 1, p. 012125, mar 2024. [Online].

Available: https://dx.doi.org/10.1088/1755-1315/1314/1/012125https://iopscience.iop.org/article/10.1088/1755-1315/1314/1/

012125

[13] J. George and E. Al., “Software Engineering: A Practitioner’s Approach,” in Software Engineering: A Practitioner’s Approach.

McGraw-Hill, 2024, pp. 1–900.

[14] S. Ghosh and D. Chakraborty, “Fuzzy-based flood risk modeling using decision flowcharts,” Environmental Modelling & Software,

vol. 165, p. 104712, 2023.

[15] V. Hadipour, F. Vafaie, and K. Deilami, “Coastal Flooding Risk Assessment Using a GIS-Based Spatial Multi-Criteria Decision

Analysis Approach,” Water, vol. 12, no. 9, p. 2379, aug 2020. [Online]. Available: https://www.mdpi.com/2073-4441/12/9/2379

[16] G. A. Rahardi, W. Muldayani, M. D. A. Wijaya, D. Setiabudi, and H. M. Firdausi, “Early Warning System Design

for Flood Disasters Using the IoT-Based Fuzzy Logic Control Method,” in 2022 International Conference on Electrical

Engineering, Computer and Information Technology (ICEECIT). IEEE, nov 2022, pp. 131–135. [Online]. Available:

https://ieeexplore.ieee.org/document/10030237/

[17] S. Tomasiello,W. Pedrycz, and V. Loia, “Contemporary Fuzzy Logic,” in Big and Integrated Artificial Intelligence, ser. Big and

Integrated Artificial Intelligence. Cham: Springer International Publishing, 2022, vol. 1, pp. 101 –140. [Online]. Available:

https://link.springer.com/10.1007/978-3-030-98974-3

[18] B. A. Beker and M. L. Kansal, “Fuzzy logic-based integrated performance evaluation of a water distribution network,”

Journal of Water Supply: Research and Technology-Aqua, vol. 71, no. 3, pp. 490–506, mar 2022. [Online]. Available:

https://iwaponline.com/aqua/article/71/3/490/87283/Fuzzy-logic-based-integrated-performance

[19] H. Chen, Z. Xu, Y. Liu, Y. Huang, and F. Yang, “Urban Flood Risk Assessment Based on Dynamic Population Distribution and

Fuzzy Comprehensive Evaluation,” International Journal of Environmental Research and Public Health, vol. 19, no. 24, p.

16406, dec 2022. [Online]. Available: https://www.mdpi.com/1660-4601/19/24/16406

[20] K. Lira, M. v. Z. de Jong, M. King, and I. Cowx, “A watershed fragility index for assessing the vulnerability of river

ecosystems,” Ecological Indicators, vol. 178, p. 113908, sep 2025. [Online]. Available: https://www.sciencedirect.com/

science/article/pii/S1470160X25008386https://linkinghub.elsevier.com/retrieve/pii/S1470160X25008386

[21] H. Akay, “Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees,” Water

Resources Management, vol. 38, no. 13, pp. 5365–5383, oct 2024. [Online]. Available: https://link.springer.com/10.1007/

s11269-024-03918-5

[22] M. Yılmaz and K. D. Alemdar, “Mapping and assessment of flood risk based on vulnerability and hazard

factors in urban areas through the integration of multi-criteria techniques and GIS: A case study in Yakutiye,

Erzurum, T¨urkiye,” Environmental Earth Sciences, vol. 84, no. 15, p. 435, jul 2025. [Online]. Available:

https://doi.org/10.1007/s12665-025-12393-zhttps://link.springer.com/10.1007/s12665-025-12393-z

[23] X. Ma, Y. Wang, Z. Tang, and S. Li, “Urban Flood Risk Assessment Based on DEMATEL-ANP Hybrid

Fuzzy Evaluation and Hydrodynamic Model,” Water, vol. 17, no. 10, p. 1494, may 2025. [Online]. Available:

https://www.mdpi.com/2073-4441/17/10/1494

[24] M. G. Voskoglou, “Fuzzy Sets, Fuzzy Logic and Their Applications 2020,” in Fuzzy Sets, Fuzzy Logic and Their Applications

2020, M. G. Voskoglou, Ed. MDPI, sep 2021, pp. 1–250. [Online]. Available: http://www.mdpi.com/books/pdfview/book/4344

[25] J. M. Mendel, “Explainable Uncertain Rule-Based Fuzzy Systems,” in Explainable Uncertain Rule-Based Fuzzy

Systems, 3rd, Ed. Cham: Springer International Publishing, 2024, pp. 1–450. [Online]. Available: https:

//doi.org/10.1007/978-3-031-35378-9https://link.springer.com/10.1007/978-3-031-35378-9

[26] Y. Liu, Q. Zhao, C. Hu, and N. Luo, “Prediction of Storm Surge Water Level Based on Machine Learning Methods,”

Atmosphere, vol. 14, no. 10, p. 1568, oct 2023. [Online]. Available: https://www.mdpi.com/2073-4433/14/10/1568

[27] C. Ni, P. S. Fam, and M. F. Marsani, “A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk

Prediction,” International Journal of Intelligent Systems, vol. 2024, no. 1, pp. 1–20, feb 2024. [Online]. Available:

https://doi.org/10.1155/2024/3562709https://www.hindawi.com/journals/ijis/2024/3562709/

[28] S. C. Roy, S. Banik, and M. Pramanik, “Performance comparison of machine learning models for monthly water level forecasting:

A case study in Indian River Basins,” Environmental Advances, vol. 13, p. 100157, 2025.

[29] Z. Zhou, Q. Liu, and J. He, “Evaluating flood forecasting performance using RMSE, MAE, and NSE: A case from the Yangtze

River,” Natural Hazards, vol. 113, no. 2, pp. 1213–1232, 2022.

[30] M. Kaur, P. D. Kaur, and S. K. Sood, “ANFIS-based flood detection and vulnerability assessment framework,” Hydrological

Sciences Journal, vol. 67, no. 15, pp. 2310–2326, nov 2022. [Online]. Available: https://doi.org/10.1080/02626667.2022.

2138759https://www.tandfonline.com/doi/full/10.1080/02626667.2022.2138759

[31] L. Khaldi, A. El Bilali, A. El Abed, N. Krakauer, and A. El Khanchoufi, “Developing an explainable

and interpretable machine learning model for flood susceptibility mapping,” Ecological Engineering & Environmental

Technology, vol. 26, no. 1, pp. 201–215, jan 2025. [Online]. Available: http://www.ecoeet.com/

Developing-an-explainable-and-interpretable-machine-learning-model-for-flood-susceptibility,195845,0,2.html

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Published

2025-11-30

Issue

Vol. 25 No. 1 (2025)

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Copyright (c) 2025 Joko Handoyo, Anton Yudhana, Sunardi Sunardi

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How to Cite

[1]
J. Handoyo, A. Yudhana, and S. Sunardi, “Flood Vulnerability Mapping in Cepu Subdistrict Using MamdaniFuzzy Inference System for Disaster Risk Reduction”, MATRIK, vol. 25, no. 1, pp. 25–38, Nov. 2025, doi: 10.30812/matrik.v25i1.5390.

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