Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia

  • Muhammad Asyroful Mujib Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia http://orcid.org/0000-0002-5061-9640
  • Bejo Apriyanto Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia
  • Fahmi Arif Kurnianto Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia http://orcid.org/0000-0002-3916-8930
  • Fahrudi Ahwan Ikhsan Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia http://orcid.org/0000-0001-9169-632X
  • Elan Artono Nurdin Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia http://orcid.org/0000-0001-8001-168X
  • Era Iswara Pangastuti Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia
  • Sri Astutik Department of Geography Education, Universitas Jember, Jl. Kalimantan 37 Jember, East Java, 68121, Indonesia

Abstract

Flood is one of the most frequent hydrometeorological disasters which leads in economic losses. The first step in flood disaster mitigation efforts is mapping vulnerable areas. Kencong District frequently affected by the annual flooding event. This study aims to assess flood hazard mapping by integrating the AHP method and Geographic Information System. This study used a descriptive quantitative approach through the correlation matrix of the AHP model for each physical environmental factor. These factors include slope, altitude, distance from the river, soil type, Topographic Wetness Index (TWI), and Curvature. Furthermore, with the Geographic Information System (GIS), the weighted overlay stage was carried out to obtain the results of flood-prone areas. Based on the AHP analysis, the most significant factors in determining flood-prone areas were the distance from rivers, slopes, and TWI. The results of flood-prone areas mapping were divided into five classes: from deficient 0.02%, low 4.26%, medium 37.11%, high 51.89%, and very high 6.72%. Validation of GIS mapping results with data in the field has an AUC value of 84%, which indicates that the prediction of the AHP-GIS model is perfect in flood-prone areas mapping in the Kencong District. The integration of AHP method and Geographic Information System in flood hazard assessment were able to produce a model to evaluate the spatial distribution of flood-prone areas.


Keywords : Flood Hazard Mapping; Multi-criteria decision analysis; AHP Model; GIS; Jember


 


Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember


Creative Commons License
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

References

Ainunisa, D., Halik, G., & Widiarti, W. Y. (2020). Pemodelan Perubahan Tataguna Lahan Terhadap Debit Banjir DAS Tanggul, Jember Menggunakan Model SWAT (Soil and Water Assessment Tool). Rekayasa Sipil, 14(2), 154–161. https://doi.org/10.21776/ub.rekayasasipil.2020.014.02.10.

Ajjur, S. B., &Mogheir, Y. K. (2020). Flood hazard mapping using a multi-criteria decision analysis and GIS (case study Gaza Governorate, Palestine). Arabian Journal of Geosciences, 13(2). https://doi.org/10.1007/s12517-019-5024-6.

Arseni, M., Rosu, A., Calmuc, M., Calmuc, V. A., Iticescu, C., & Georgescu, L. P. (2020). Development of flood risk and hazard maps for the lower course of the Siret River, Romania. Sustainability (Switzerland), 12(16). https://doi.org/10.3390/su12166588.

Bachri, S., Aldianto, Y. E., Sumarmi;, Kresno, S. B. U. ., & Naufal, M. F. (2021). Flood Modelling of Badeng River Using HEC-RAS in Singojuruh Sub-District, Banyuwangi Regency, East Java, Indonesia. Jurnal Geografi, 13(1), 76–87. https://doi.org/10.24114/jg.v13i1.19211.

Balica, S. F., Popescu, I., Beevers, L., & Wright, N. G. (2013). Parametric and physically based modelling techniques for flood risk and vulnerability assessment: A comparison. Environmental Modelling and Software, 41, 84–92. https://doi.org/10.1016/j.envsoft.2012.11.002.

Bathrellos, G. D., Skilodimou, H. D., Chousianitis, K., Youssef, A. M., & Pradhan, B. (2017). Suitability estimation for urban development using multi-hazard assessment map. Science of the Total Environment, 575, 119–134. https://doi.org/10.1016/j.scitotenv.2016.10.025.

Bellos, V. (2012). Ways for flood hazard mapping in urbanised environments : A short literature review. Water Utility Journal, January 2012, 25–31. https://www.ewra.net/wuj/pdf/WUJ_2012_04_03.pdf.

BNPB. (2019). Katalog Desa/Kelurahan Rawan Banjir (Kelas Kerawanan Tinggi dan Sedang). Retrieved from https://bnpb.go.id/.

BNPB. (2021). Indeks risiko bencana Indonesia (IRBI) tahun 2020. Retrieved from https://bnpb.go.id/.

Carmo, J. S. A. (2020). Physical Modelling vs. Numerical Modelling: Complementarity and Learning. Preprints (Www.Preprints.Org), July. https://doi.org/10.20944/preprints202007.0753.v1.

Chakraborty, S., & Mukhopadhyay, S. (2019). Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Natural Hazards, 99(1), 247–274. https://doi.org/10.1007/s11069-019-03737-7.

Costache, R. (2019). Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management. Water Resources Management, 33(9), 3239–3256. https://doi.org/10.1007/s11269-019-02301-z.

Costache, R., Arabameri, A., Elkhrachy, I., Ghorbanzadeh, O., & Pham, Q. B. (2021). Detection of areas prone to flood risk using state-of-the-art machine learning models. Geomatics, Natural Hazards and Risk, 12(1), 1488–1507. https://doi.org/10.1080/19475705.2021.1920480.

Dahri, N., & Abida, H. (2017). Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environmental Earth Sciences, 76(7). https://doi.org/10.1007/s12665-017-6619-4.

Danumah, J. H., Odai, S. N., Saley, B. M., Szarzynski, J., Thiel, M., Kwaku, A., Kouame, F. K., & Akpa, L. Y. (2016). Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenvironmental Disasters, 3(1). https://doi.org/10.1186/s40677-016-0044-y.

Das, S. (2018). Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences, 11(19). https://doi.org/10.1007/s12517-018-3933-4.

Das, S. (2019). Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sensing Applications: Society and Environment, 14, 60–74. https://doi.org/10.1016/j.rsase.2019.02.006.

Das, S. (2020). Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sensing Applications: Society and Environment, 20, 100379. https://doi.org/10.1016/j.rsase.2020.100379.

Das, S., & Pardeshi, S. D. (2018). Comparative analysis of lineaments extracted from Cartosat, SRTM and ASTER DEM: a study based on four watersheds in Konkan region, India. Spatial Information Research, 26(1), 47–57. https://doi.org/10.1007/s41324-017-0155-x.

Djalante, R., & Garschagen, M. (2017). A Review of Disaster Trend and Disaster Risk Governance in Indonesia:1900-2015. In Disaster Risk Reduction in Indonesia.https://doi.org/10.1007/978-3-319-54466-3_2.

Eini, M., Kaboli, H. S., Rashidian, M., & Hedayat, H. (2020). Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction, 50, 101687. https://doi.org/10.1016/j.ijdrr.2020.101687.

Feng, L. H., & Lu, J. (2010). The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications, 37(4), 2974–2977. https://doi.org/10.1016/j.eswa.2009.09.037.

Fernández, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1–4), 90–98. https://doi.org/10.1016/j.enggeo.2009.12.006.

Hammami, S., Zouhri, L., Souissi, D., Souei, A., Zghibi, A., Marzougui, A., & Dlala, M. (2019). Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences, 12(21). https://doi.org/10.1007/s12517-019-4754-9.

Handini, D. R., Hidayah, E., & Halik, G. (2021). Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method. Geosfera Indonesia, 6(2), 157–172. https://doi.org/https://doi.org/10.19184/geosi.v6i2.24173.

Haq, T., Halik, G., & Hidayah, E. (2020). Flood routing model using integration of Delft3D and GIS (case study: Tanggul watershed, Jember). AIP Conference Proceedings, 2278(October). https://doi.org/10.1063/5.0014607.

Ho, L. T. K., & Umitsu, M. (2011). Micro-landform classification and flood hazard assessment of the Thu Bon alluvial plain, central Vietnam via an integrated method utilizing remotely sensed data. Applied Geography, 31(3), 1082–1093. https://doi.org/10.1016/j.apgeog.2011.01.005.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning, With application in R. Springer: US.

Ji, J., Choi, C., Yu, M., & Yi, J. (2012). Comparison of a data-driven model and a physical model for flood forecasting. WIT Transactions on Ecology and the Environment, 159, 133–142. https://doi.org/10.2495/FRIAR120111.

Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope-Evros region, Greece. Science of the Total Environment, 538, 555–563. https://doi.org/10.1016/j.scitotenv.2015.08.055.

Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), 251–264. https://doi.org/10.1007/s12665-011-1504-z.

Mahmoud, S. H., & Gan, T. Y. (2018). Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production, 196, 216–229. https://doi.org/10.1016/j.jclepro.2018.06.047.

Mudashiru, R. B., Sabtu, N., Abustan, I., & Balogun, W. (2021). Flood hazard mapping methods: A review. Journal of Hydrology, 603(PA), 126846. https://doi.org/10.1016/j.jhydrol.2021.126846.

Olii, M. R., Olii, A., & Pakaya, R. (2021). The integrated spatial assessment of the flood hazard using AHP-GIS: The case study of gorontalo regency. Indonesian Journal of Geography, 53(1), 126–135. https://doi.org/10.22146/IJG.59999.

Papaioannou, G., Vasiliades, L., & Loukas, A. (2015). Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping. Water Resources Management, 29(2), 399–418. https://doi.org/10.22146/IJG.59999.

Pelling, M. (2003). The vulnerability of cities : natural disasters and social resilience. In Earthscan Publications Ltd. https://doi.org/10.1039/J19660001254.

Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996. https://doi.org/10.1007/s11069-012-0217-2.

Pourghasemi, H. reza, Beheshtirad, M., & Pradhan, B. (2014). A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk, 7(2), 861–885. https://doi.org/10.1080/19475705.2014.984247.

Public Works Department of Highways and Natural Resources of Jember Regency Natural Resources Coordinator Area of Kencong and Gumukmas Districts. (2018). Data on the Social Impact of the Flood in Kencong District in 2018. (Unpublished).

Public Works Department of Highways and Natural Resources of Jember Regency Natural Resources Coordinator Area of Kencong and Gumukmas Districts. (2021). Chronology and Characteristics of Flooding Along the Embankment River. (Unpublished).

Rahmati, O., Zeinivand, H., & Besharat, M. (2016). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000–1017. https://doi.org/10.1080/19475705.2015.1045043.

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5.

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I.


Saaty, T. L. (2004). Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1), 1–35. https://doi.org/10.1007/s11518-006-0151-5.
Samanta, S., Koloa, C., Pal, D. K., & Palsamanta, B. (2016). Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA). Hydrology, 3(3), 1–13. https://doi.org/10.3390/hydrology3030029.

Satty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. In McGrow, New York. https://doi.org/10.1201/9780429504419-2.

Seejata, K., Yodying, A., Wongthadam, T., Mahavik, N., & Tantanee, S. (2018). Assessment of flood hazard areas using Analytical Hierarchy Process over the Lower Yom Basin, Sukhothai Province. Procedia Engineering, 212, 340–347. https://doi.org/10.1016/j.proeng.2018.01.044.

Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., & Shirzadi, A. (2018). Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of Environmental Management, 217, 1–11. https://doi.org/10.1016/j.jenvman.2018.03.089.

Siregar, R. I., & Indrawan, I. (2017). Studi Komparasi Pemodelan 1-D (Satu Dimensi) Dan 2-D (Dua Dimensi) Dalam Memodelkan Banjir Das Citarum Hulu. Educational Building, 3(2). https://doi.org/10.24114/eb.v3i2.8255.

Suwarti, T., & Suharsono. Pusat Penelitian dan Pengembangan Geologi. (1993.). Peta geologi lembar Lumajang, Jawa = Geological map of the Lumajang quadrangle, Jawa [peta] / oleh T. Suwarti dan Suharsono. Bandung: Pusat Penelitian dan Pengembangan Geologi.

Soulsby, C., Tetzlaff, D., & Hrachowitz, M. (2010). Spatial distribution of transit times in montane catchments: Conceptualization tools for management. Hydrological Processes, 24(22), 3283–3288. https://doi.org/10.1002/hyp.7864.

Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034.

Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101. https://doi.org/10.1016/j.catena.2014.10.017.

Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling and Software, 90, 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006.

Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and nave bayes models. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/974638.

USDA. (1999). Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, 2nd Edition.https://doi.org/10.1201/9780429445552-38.

Vorogushyn, S., Lindenschmidt, K. E., Kreibich, H., Apel, H., & Merz, B. (2012). Analysis of a detention basin impact on dike failure probabilities and flood risk for a channel-dike-floodplain system along the river Elbe, Germany. Journal of Hydrology, 436–437, 120–131. https://doi.org/10.1016/j.jhydrol.2012.03.006.

Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., Drobnjak, S., Bui, D. T., & Duan, H. (2019). A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sensing, 11(1). https://doi.org/10.3390/rs11010062.

Youssef, A. M., Pradhan, B., Gaber, A. F. D., & Buchroithner, M. F. (2009). Geomorphological hazard analysis along the Egyptian Red Sea coast between Safaga and Quseir. Natural Hazards and Earth System Science, 9(3), 751–766. https://doi.org/10.5194/nhess-9-751-2009.

Youssef, A. M., & Hegab, M. A. (2019). Flood-Hazard Assessment Modeling Using Multicriteria Analysis and GIS. In Spatial Modeling in GIS and R for Earth and Environmental Sciences (Issue 2017). Elsevier Inc. https://doi.org/10.1016/b978-0-12-815226-3.00010-7.

Youssef, A. M., Pradhan, B., & Sefry, S. A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(1), 1–16. https://doi.org/10.1007/s12665-015-4830-8.

Zheng, Z., Qi, S., & Xu, Y. (2013). Questionable frequent occurrence of urban flood hazards in modern cities of China. Natural Hazards, 65(1), 1009–1010. https://doi.org/10.1007/s11069-012-0397-9.

Zhu, X. (2016). GIS for Environmental Applications: a practical approach. New York : Routledge.

Zumel, N., & Mount, J. (2014). Practical Data Science with R. New York: Simon and Schuster. https://doi.org/10.2741/4268.
Published
2021-12-22
How to Cite
MUJIB, Muhammad Asyroful et al. Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia. Geosfera Indonesia, [S.l.], v. 6, n. 3, p. 353-376, dec. 2021. ISSN 2614-8528. Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/21668>. Date accessed: 03 july 2024. doi: https://doi.org/10.19184/geosi.v6i3.21668.
Section
Original Research Articles