Author(s)

Ataullah Darzar^*, Mohammad Karam Ikram

Affiliation(s)

Engineering Faculty, Kandahar University, Kandahar, Afghanistan.

Corresponding Author

Ataullah Darzar

ABSTRACT

Floods, which are common than other natural disasters like earthquakes, heavy precipitations, and droughts, are one the primary effects of global climate change and have major effects on human safety, sustainable development, and economic growth. As climate warming and intensifying hydrologic cycle worsen, global flooding risks may increase, potentially impacting Afghanistan as well. Severe flooding being caused by rising temperatures, erratic rainfall patterns, and extreme weather in Afghanistan, especially in the region of Kandahar. Despite the significance of identifying and mapping flood – prone areas, this province has not participated in any previous studies done on the topic at hand. Therefore, the aim of this research was to develop a flood susceptibility map for Kandahar province and identified flood – prone areas with high levels of occurrence by integrating Geographic Information System (GIS) and Multi – Criteria Decision – Making (MCDM) method, with Analytic Hierarchy Process (AHP). To achieve the study’s goal, 11 Flood Causative Factors (FCFs), such as runoff potential, slope, rainfall, flow accumulation, distance from rivers, topographic wetness index (TWI), drainage density, lithology, Digital elevation model (DEM), sediment transport index (STI), and curvature, were weighted and overlayed. The resulting map depicted five different levels of susceptibility to flooding: least, low, moderate, high, and very high. The model’s final map of flood susceptibility was found to be in line with past flood occurrences in the study area, demonstrating the successful outcome of the methodology utilized to locate and map flood – prone areas.

KEYWORDS

Flood susceptibility map; GIS; MCDM; AHP; Kandahar Province; Afghanistan

CITE THIS PAPER

Ataullah Darzar, Mohammad Karam Ikram. Flood susceptibility mapping using GIS – based multi – criteria decision – making “MCDM” method: a case study of Kandahar Province, Afghanistan. Frontiers in Environmental Research. 2024, 2(2): 35-53. DOI: 10.61784/fer3004.

REFERENCES

[1] Balasubramanian, A. Floods as Natural Hazards. Presentation in August. 2005. DOI: https://doi.org/10.13140/RG.2.2.18898.96962.

[2] Duan, Y, Xiong J N, Cheng W M, et al. Increasing Global Flood Risk in 2005 – 2020 from a Multi – Scale Perspective. Journal of Remote Sensing. 2022, 14(21),5551. DOI: https://doi.org/10.3390/rs14215551. 

[3] Njock, P G A, Shen S L, Zhou A N, et al. Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t–SNE model. Journal of Soil Dynamic and Earthquake Engineering, 2020, 130: 105988. DOI: https://doi.org/10.1016/j.soildyn.2019.105988.

[4] Jongman, B, Kreibich, H, Apel, H, et al. Comparative flood damage model assessment: towards a European approach. Natural Hazards and Earth System Sciences, 2012, 12(12): 3733-3752. DOI: https://doi.org/10.5194/nhess-12-3733-2012.

[5] Zang Q, Li J F, Singh, V P, et al. Spatio–temporal relations between temperature and precipitation regimes: Implications for temperature–induced changes in the hydrological cycle. Journal of Global and Planetary Change, 2013, 111: 57-76. DOI: https://doi.org/10.1016/j.gloplacha.2013.08.012.

[6] Rentschler, J, Salhab M, Jafino, B A, et al. Flood exposure and poverty in 188 countries. Nature Communications. 2022. https://doi.org/10.1038/s41467-022-30727-4.

[7] Hirabayashi, Y, Mahendran, R, Koirala, S, et al. Global ?ood risk under climate change. Journal of Nature Climate Chang, 2013, 3(9): 816-821. DOI: https://doi.org/10.1038/nclimate1911.

[8] Chris Courtne. The Nature of Disaster in China: The 1931 Yangzi River Flood. East Asian Science Technology and Medicine, 2021, 53(1-2): 106-110. DOI: https://doi.org/10.1163/26669323-53010004. 

[9] Hooke, J, Mant, J. Geomorphological impacts of a flood event on ephemeral channels in SE Spain. Journal of Geomorphology, 2000, 34(3-4): 163-180. DOI: https://doi.org/10.1016/S0169-555X(00)00005-2.

[10] Maillet, G M, Vella C, Berne, S, et al. Morphological changes and sedimentary processes induced by the December 2003 flood event at the present mouth of the Grand Rhane River (Southern France). Marine Geology. 234, 2006, (1-4): 159-177. DOI: https://doi.org/10.1016/j.margo.2006.09.025.

[11] LIasat, M, del, C, Rigo, T, et al. The “Montserrat-2000” flash-flood event: a comparison with the flood that have occurred in the Northeastern Iberian Peninsula since the 14th Century. International Journal of Climatology, 2003, 23(4): 453-469. DOI: https://doi.org/10.1002/joc.888.

[12] The United Nation Office. Flash floods in southern Afghanistan kill at least 20. 2 March 2019. https://WWW.thenews.com.pk/latest/438842–at–least–20–killed–by–flash–floods–in–southern–afghanistan. 

[13] AFP. Afghan search operations continue as flash floods kill more than 160. 27 Aug 2020. https://afghanistan.asia–news.com/en–GB/articles/cnmi_st/features/2020/08/27/feature–02.

[14] Afghan Red Crescent. Heavy Floods Have Damaged Spin Boldak of Kandahar Province, Southern Afghanistan. 1 Aug 2022. https://reliefweb.int/report/afghanistan/heavy–floods–have–damaged–many–lands–gardans–farms–and–houses–spin–boldak–district–kandahar–province–southern–afghanistan. 

[15] Kuwait News Agency. Floods in Afghanistan and Pakistan. 23 April 2024. https://www.aljazeera.com/amp/news/2024/4/14/dozens–killed–in–afghanistan–as–heavy–rains–set–off–flash–floods.

[16] OCHA, Floods hit Eastern and Northeastern Afghanistan, 17 July 2024. https://reliefweb.int/disaster/fl–2024–000038–afg. 

[17] Kaya, C M, Derin, L. Parameters and methods used in flood susceptibility mapping: a review. Journal of Water & Climate Change, 2024, 14(6): 1935-1960. DOI: https://doi.org/10.2166/wcc.2023.035.

[18] Dai, F C, Lee, C F. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 2002, 42: 213-228. DOI: https://doi.org/10.1016/S0169-555X(01)00087-3.

[19] Kabalc?, E. Flexible Calculation Method–I. 2022. https://ekblc.files.workpress.com/2013/09/esnekhesaplamaya–giric59.pdf 

[20] Zadeh, L A. Fuzzy logic: issues, contentions and perspectives, Proceedings of ICASS’94. IEEE International Conference on Acoustics and Signal Processing, Adelaide, SA, Australia 1994, 6, V1-183. DOI: https://doi.org/10.1145/197530.197667. 

[21] Buckley, J J. Hayashi, Y. Fuzzy neural networks. A survey. Fuzzy Sets and Systems, 1994, 66: 1-13. DOI: https://doi.org/10.1016/0165-0114(94)90297-6.

[22] Malczewski, J. GIS and Multicriteria Decision Analysis. John Wiley & Sons, New York, 1999.

[23] Hong, H, Panahi, M, Shirzadi, A, et al. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ, 2018, 621: 1124-1141. DOI: https://doi.org/10.1016/j.scitotenv.2017.10.114.

[24] Ajibade, F O, Ajibade, T F, Idowu, T E, et al. Flood-prone area mapping using GIS-based analytical hierarchy frameworks for Ibadan city, Nigeria. Journal of Multi-Criteria Decision-Analysis, 2021, 28(5-6): 283-295. DOI: https://doi.org/10.1002/mcda.1759. 

[25] Shreve R L. Statical law of stream numbers. The Journal of Geology, 1966, 74(1): 17-37. DOI: https://doi.org/10.1086/627137.

[26] Strahler A N. Quantitative analysis of watershed geomorphology. Transactions, American Geophysical Union, 1957, 38(6): 913-920. DOI: https://dx.doi.org/10.1029/TR038i006p00913.

[27] Horton, R E. Drainage-basin characteristics. Trans Am Geophys Union, 1932, 13(1): 350-361. DOI: https://doi.org/10. 1029/TR013i001p00350.

[28] Youssef, A M, Pradhan, B, Hassan, A M. Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences, 2011, 62(3): 611-623. DOI: https://doi.org/10.3390/w11020364. 

[29] Astutik, S, Pangastuti, E I, Nurdin, E A, et al. Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia. Geosfera Indonesia, 2021, 6(3): 353. DOI: https://doi.org/10.19184/geosi.v6i3.21668. 

[30] Hammami, S, Zouhri, L, Souissi, D, et al. Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences. 2019, 12(21): 1-16. DOI: https://doi.org/10.1007/s12517-019-4754-9.

[31] Negese, A, Worku, D., Shitaye, A, et al. Potential flood?prone area identification and mapping using GIS?based multi?criteria decision?making and analytical hierarchy process in Dega Damot district, northwestern Ethiopia. Applied Water Science, 2022, 12, 255. DOI: https://doi.org/10.1007/s13201-022-01772-7. 

[32] Allafta, H, Opp, C. GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran. Geomat Nat Haz Risk. 2021, 12(1): 2087-2116. DOI: https://doi.org/10.1080/19475705.2021.1955755.

[33] USDA-SCS. Soil survey of travis county texas agricultural experiment station. USDA soil conservation service, Washington, 1974.

[34] Zeng Z, Tang G Q, Hong Y, et al. Developmwnt of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrological applications. Remote Sensing Letters, 2017, 8(6): 528-536. https://dx.doi.org/10.1080/2150704X.2017.1297544.

[35] Khosravi, K, Pham, B T, Chapi, K, et al. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment, 2018, 627: 744-755. DOI: https://doi.org/10.1016/j.scitotenv.2018.01.266.

[36] U.S Environmental Protection Agency. Soil Runoff Potential. Indicator Reference Sheet – March 6, 2022. https://WWW.epa.gov/system/files/documents/2022–3/soil–runoff–potential–indicator–reference–sheet–20220306.pdf 

[37] Lee, S, Rezaie, F. Data used for GIS-based flood susceptibility mapping. Data Geol Ecol Oceanogr Space Sci Polar Sci, 2022, 41: 1-15. DOI: https://doi.org/10.22761/DJ2022.4.1.001.

[38] Das, S, Gupta, A. Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geosci Front, 2021, 12(5): 101206. DOI: https://doi.org/10.1016/j.gsf.2021.101206.

[39] Mitra, R, Das, J. A comparative assessment of flood susceptibility modelling of GIS – based TOPSIS, VIKOR, and EDAS techniques in the sub–Himalayan foothills region of Eastern India. Environmental Science and Pollution Research, 2023, 30: 16036-16067. DOI: https://doi.org/10.1007/s11356–022–23168–5.

[40] Kron, W. Flood Risk = Hazard ? Values ? Vulnerability. Water International, 2005, 30(1): 58-68. DOI: https://doi.org/10.1080/02508060508691837.

[41] Elkhrachy, I. Flash flood hazard mapping using satellite images and GIS tools: a case study of  Najran City, Kingdom of Saudi Arabia (KSA). The Egyptian Journal of Remote Sensing and Space Science, 2015, 18 (2): 261-278. DOI: https://doi.org/10.1016/j.ejrs.2015.06.007.

[42] Mahmoud, S H, Gan, T Y. Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod, 2018, 196: 216-229. DOI: https://doi.org/10.1016/j.jclepro.2018.06.047.

[43] Islam, M M, Sado, K. Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. Hydrological Sciences Journal, 2000, 45(3): 337-355. DOI: https://doi.org/10.1080/02626660009492334.

[44] Bui, D T, Tsangaratos, P, Ngo, P T T, et al. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Science of The Total Environment, 2019, 668: 1038-1054. DOI: https://doi.org/10.1016/j.scitotenv.2019.02.422.

[45] Mirzaei, S, Vafakhah, M, Pradhan, B, et al. Flood susceptibility assessment using extreme gradient boosting (EGB), Iran. Earth Science Informatics, 2021, 14(1): 51-67. DOI: https://doi.org/10.1007/s12145-020-00530-0.

[46] Pradhan, B. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 2010, 45(10): 1244-1256. DOI: https://doi.org/10.1016/j.asr.2010.01.006.

[47] Tehrany, M S, Pradhan, B, Jebur, M N. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 2015a, 29(4): 1149-1165. DOI: https://doi.org/10. 1007/s00477-015-1021-9.

[48] Tehrany, M S, Pradhan, B, Mansor, S, et al. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 2015b, 125: 91-101. DOI: https://doi.org/10.1016/j.catena.2014.10.017.

[49] Tehrany, M S, Lee, M J, Pradhan, B, et al. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences, 2014b, 72(10): 4001-4015. DOI: https://doi.org/10.1007/s12665-014-3289-3.

[50] Arslankaya, D, G?raltay, K. ?ok Kriterli Karar Verme Y?ntemlerinde Güncel Yaklas?? mlar (Current Approaches in Multi-Criteria Decision-Making Methods). Iksad Publications, Ankara, 2019.

[51] Saaty T L. The analytic hierarchy process planning, priority setting, resource allocation. McGraw Hill, New York, 1980. https://WWW.worldcat.org/research?qt=worldcat_org_all&q=0070543712.

[52] Mardani A, Jusoh A, Nor K M, et al. Multiple criteria decision-making techniques and their applications-a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istrazivanja, 2015, 28(1): 516-571. DOI: https://dx.doi.org/10.1080/1331677X.2015.1075139.

[53] Marinoni O. Implementation of the analytic hierarchy process with VBA in ArcGIS. Computers and Geosciences, 2004, 30(6): 637-646. DOI: https://dx.doi.org/j.cageo.2004.03.010.

[54] Saaty T L, Vargas L G. Prediction, projection and forecasting. Kluwer Academic Publishers, Dordrecht. 1991. DOI: https://dx.doi.org/10.1007/978–94–015–7952–0.