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Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique—a case study of Patan district Gujarat, India

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Abstract

Assessing and map** flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district’s land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

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Data availability

The datasets analyzed during the current study are available at the Copernicus Open Access Hub (https://scihub.copernicus.eu/dhus/#/home), USGS website (https://earthexplorer.usgs.gov/), NASA Prediction of Worldwide Energy Resources (https://power.larc.nasa.gov/data-access-viewer/), Soil and Land Use Survey of India field survey data (https://slusi.dacnet.nic.in/), and Bhuvan (https://bhuvan.nrsc.gov.in/) that are cited in this manuscript. The detail of dataset used in the study is given in Table 2.

References

  • Aggarwal, S., Thakur, P., & Dadhwal, V. (2009). Remote sensing and GIS applications in flood management. Journal of Hydrological Research and Development, Theme Flood Management., 24, 145–158.

    Google Scholar 

  • Ali, S. A., Khatun, R., Ahmad, A., & Ahmad, S. N. (2019). Application of GIS based analytic hierarchy process and frequency ratio model to food vulnerable map** and risk area estimation at Sundarban region. India. Modeling Earth Systems and Environment, 5(3), 1083–1102. https://doi.org/10.1007/s40808-019-00593-z

    Article  Google Scholar 

  • Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., & Ghorbani, M. A. (2020). GIS-based comparative assessment of flood susceptibility map** using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin. Slovakia. Ecological Indicators, 117, 106620. https://doi.org/10.1016/j.ecolind.2020.106620

    Article  Google Scholar 

  • Andrew, T., Luca, V., Montserrat, M.F., Brian, D. (2018). European Commission, Joint Research Centre, Inform global risk index: Results 2018, Publications Office, 2018, https://data.europa.eu/doi/https://doi.org/10.2760/754353

  • Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility map** in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  • Babu, R., Dhyani, B. L., & Kumar, N. (2004). Assessment of erodibility status and refined Iso- Erodent Map of India. Indian Journal of Soil Conservation, 32(2), 171–177.

    Google Scholar 

  • Ballerine, C. (2017). Topographic wetness index urban flooding awareness act action support. Illinois State Water Survey, Prairie Research Institute University of Illinois at Urbana-Champaign. Contract Report, 2017–02, 1–17.

    Google Scholar 

  • Barredo, J. I., & Engelen, G. (2010). Land use scenario modeling for flood risk mitigation. Sustainability, 2(5), 1327–1344. https://doi.org/10.3390/su2051327

    Article  Google Scholar 

  • Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Science Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  • Bilskie, M. V., Hagen, S. C., Medeiros, S. C., & Passeri, D. L. (2014). Dynamics of sea level rise and coastal flooding on a changing landscape. Geophysical Research Letters, 41, 927–934. https://doi.org/10.1002/2013GL058759

    Article  Google Scholar 

  • Bui, D. T., Ngo, P. T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V., & Samui, P. (2019). A novel hybrid approach base on a swarm intelligence optimized extreme learning machine for flash flood susceptibility map**. CATENA, 179, 184–196. https://doi.org/10.1016/j.catena.2019.04.009

    Article  Google Scholar 

  • Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility map** using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability, 8(9), 948. https://doi.org/10.3390/su8090948

    Article  Google Scholar 

  • Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651(2), 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064

    Article  CAS  Google Scholar 

  • Costache, R. (2019). Flash-flood Potential Index map** using weights of evidence, decision trees models and their novel hybrid integration. Stochastic Environmental Research and Risk Assessment, 3, 1375–1402. https://doi.org/10.1007/s00477-019-01689-9

    Article  Google Scholar 

  • CWC. (2018). Central water commission annual report 2018–2019. https://cwc.gov.in/sites/default/files/arcwc2018-19.pdf Accessed 17 Dec 2022

  • Das, S. (2018). Geographic information system and AHP based flood hazard zonation of Vaitarna basin. Maharashtra India. Arab Journal of Geosciences, 11, 576. https://doi.org/10.1007/s12517-018-3933-4

    Article  Google Scholar 

  • Das, S. (2020). Flood susceptibility map** 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

    Article  Google Scholar 

  • Das, B., Pal, S. C., Malik, S., & Chakrabortty, R. (2019). Living with foods through geospatial approach: A case study of Arambag CD Block of Hugli District, West Bengal. India. SN Applied Sciences, 1(4), 329. https://doi.org/10.1007/s42452-019-0345-3

    Article  Google Scholar 

  • Dewan, A. M., Islam, M. M., Kumamoto, T., & Nishigaki, M. (2007). Evaluating food hazard for land-use planning in greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resources Management, 21, 1601–1612. https://doi.org/10.1007/s11269-006-9116-1

    Article  Google Scholar 

  • Dhiman, R., Vishnu Radhan, R., Eldho, T. I., & Inamdar, A. (2019). Flood risk and adaptation in Indian coastal cities: Recent scenarios. Applied Water Science, 9(1), 5. https://doi.org/10.1007/s13201-018-0881-9

    Article  Google Scholar 

  • Di Risio, M., Bruschi, A., Lisi, I., Pesarino, V., & Pasquali, D. (2017). Comparative analysis of coastal flooding vulnerability and hazard assessment at national scale. Journal of Marine Science and Engineering, 5(4), 51. https://doi.org/10.3390/jmse5040051

    Article  Google Scholar 

  • Drobne, S., & Lisec, A. (2009). Multi-attribute decision analysis in GIS: Weighted linear combination and ordered weighted averaging. Informatica, 33(4), 459–474.

    Google Scholar 

  • Dubey, A. K., Kumar, P., Chembolu, V., Dutta, S., Singh, R. P., & Rajawat, A. S. (2021). Flood modelling of a large transboundary river using WRF-Hydro and microwave remote sensing. Journal of Hydrology, 598, 126391. https://doi.org/10.1016/j.jhydrol.2021.126391

    Article  Google Scholar 

  • Duc TT (2006) Using Gis and Ahp technique for land-use suitability analysis. In: International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, pp 1–6.

  • Dung, N. B., Long, N. Q., & Goyal, R. (2022). The role of factors affecting flood hazard zoning using analytical hierarchy process: A review. Earth Systems and Environment, 6, 697–713. https://doi.org/10.1007/s41748-021-00235-4

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ghorbani Nejad, S., Falah, F., Daneshfar, M., Haghizadeh, A., & Rahmati, O. (2017). Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International, 32(2), 167–187. https://doi.org/10.1080/10106049.2015.1132481

    Article  Google Scholar 

  • Ghosh, A., & Kar, S. K. (2018). Application of analytical hierarchy process (AHP) for food risk assessment: A case study in Malda district of West Bengal, India. Natural Hazards, 94, 349–368. https://doi.org/10.1007/s11069-018-3392-y

    Article  Google Scholar 

  • Gourav, P., Kumar, R., Gupta, A., & Arif, M. (2020). Flood hazard zonation of Bhagirathi river basin using multi-criteria decision-analysis in Uttarakhand. India. International Journal on Emerging Technologies, 11(1), 62–71.

    Google Scholar 

  • Haghizadeh, A., Siahkamari, S., Hamzeh Haghiabi, A., & Rahmati, O. (2017). Forecasting flood-prone areas using Shannon’s entropy model. Journal of Earth System Science, 126, 39. https://doi.org/10.1007/s12040-017-0819-x

    Article  Google Scholar 

  • Harshasimha, A. C., & Bhatt, C. M. (2023). Flood vulnerability map** using Max Ent Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District. Assam. Environmental Sciences Proceedings, 25(1), 73. https://doi.org/10.3390/ECWS-7-14301

    Article  Google Scholar 

  • Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A. X., & Kazakis, N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, 621, 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114

    Article  CAS  Google Scholar 

  • Hu, S., Cheng, X., Zhou, D., & Zhang, H. (2017). GIS-based food risk assessment in suburban areas: A case study of the Fangshan District, Bei**g. Natural Hazards, 87, 1525–1543. https://doi.org/10.1007/s11069-017-2828-0

    Article  Google Scholar 

  • Hurtado-Pidal, J., Acero Triana, J. S., Espitia-Sarmiento, E., & Jarrín-Pérez, F. (2020). Flood hazard assessment in data-scarce watersheds using model coupling, event sampling, and survey data. Water, 12(10), 2768. https://doi.org/10.3390/w12102768

    Article  Google Scholar 

  • Indrayani, P., Mitani, Y., Djamaluddin, I., Ikemi, H. (2018). Spatial-temporal vulnerability and risk assessment model for urban food scenario. ASM Science Journal, 11(Special Issue 3):233–245Joshi, J.R., Soni, N.K.P., Kaushik R.M., Gohil, G.S. (2021). A critical analysis of water logging problem in Bhal region of Gujarat, India. International Journal of Creative Research Thoughts, 9(5): 191–196.

  • Kale, V. S. (2004). Floods in India: Their frequency and pattern. In K. S. Valdiya (Ed.), Co** with Natural Hazards: Indian Context (pp. 91–103). Orient Longman.

  • 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

    Article  CAS  Google Scholar 

  • Khan, Z.A. and Jhamnani, B. (2023). Development of flood susceptibility map using a GIS-based AHP approach: A novel case study on Idukki district, India. Journal of Spatial Science, https://doi.org/10.1080/14498596.2023.2236051

  • Khosravi, K., Nohani, E., Maroufnia, E., & Pourghasemi, H. R. (2016a). A GIS-based food susceptibility assessment and its map** in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2

    Article  Google Scholar 

  • Khosravi, K., Pourghasemi, H. R., Chapi, K., & Bahri, M. (2016b). Flash flood susceptibility analysis and its map** using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental Monitoring and Assessment, 188, 656. https://doi.org/10.1007/s10661-016-5665-9

    Article  Google Scholar 

  • Koukis, G., Tsiambaos, G., Sabatakakis, N. (1994). Slope movements in the Greek territory: A statistical approach. In: Proceedings of 7th International Congress International association of Engineering Geology, (vol. VI, pp. 4621–4628).

  • Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management and a GIS modelling method to assess flood-hazard areas—A case study. Hydrological Sciences Journal, 56(2), 212–225. https://doi.org/10.1080/02626667.2011.555836

    Article  Google Scholar 

  • Li, W., Lin, K., Zhao, T., Lan, T., Chen, X., Du, H., & Chen, H. (2019). Risk assessment and sensitivity analysis of flash floods in ungauged basins using coupled hydrologic and hydrodynamic models. Journal of Hydrology, 572, 108–120. https://doi.org/10.1016/j.jhydrol.2019.03.002

    Article  Google Scholar 

  • Li, Z., Liu, H., Luo, C., & Fu, G. (2021). Assessing surface water flood risks in urban areas using machine learning. Water, 13(24), 3520. https://doi.org/10.3390/w13243520

    Article  Google Scholar 

  • Li**, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle. China. Plos ONE, 13(7), e0200493. https://doi.org/10.1371/journal.pone.0200493

    Article  CAS  Google Scholar 

  • Luu, C., Von Meding, J., & Kanjanabootra, S. (2018). Assessing flood hazard using flood marks and analytic hierarchy process approach: A case study for the 2013 flood event in Quang Nam. Vietnam. Natural Hazards, 90, 1031–1050. https://doi.org/10.1007/s11069-017-3083-0

    Article  Google Scholar 

  • Lyu, H. M., Shen, S. L., Zhou, A., & Yang, J. (2020). Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP. Science of The Total Environment, 717, 135310. https://doi.org/10.1016/j.scitotenv.2019.135310

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Malczewski, J. (1999). GIS and Multicriteria Decision Analysis. John Wiley and Sons.

    Google Scholar 

  • Millet, I., & Wedley, W. C. (2002). Modelling risk and uncertainty with the analytic hierarchy process. The Journal of Multi-Criteria Decision Analysis (JMCDA), 11(2), 97–107. https://doi.org/10.1002/mcda.319

    Article  Google Scholar 

  • Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Computers, Environment and Urban Systems, 49, 54–65. https://doi.org/10.1016/j.compenvurbsys.2014.10.001

    Article  Google Scholar 

  • Nayak, S., & Bhaskaran, P. K. (2014). Coastal vulnerability due to extreme waves at Kalpakkam based on historical tropical cyclones in the Bay of Bengal. International Journal of Climatology, 34(5), 1460–1471. https://doi.org/10.1002/joc.3776

    Article  Google Scholar 

  • NDMA, (2017). Gujarat Flood 2017, A case study. Available at: https://gidm.gujarat.gov.in/sites/default/files/educate_your_self_document/Gujarat%20Flood%202017%20-%20A%20Case%20Study%20by%20NDMA%20%26%20GIDM_2.pdf [Accessed on 19 March 2022]

  • Nguyen, B., Minh, D., Ahmad, A., & Nguyen, Q. (2020). The role of relative slope length in flood hazard map** using Ahp And Gis (case study: Lam River Basin, Vietnam). Geography, Environment, Sustainability, 13(2), 115–123. https://doi.org/10.24057/2071-9388-2020-48

    Article  Google Scholar 

  • Nikolaou, N. (1997). Rain and landslide manifestation correlation in Korinthos country, Greece. Inter. Symposium on Engineering Geology and the Environment, Athens, 1, 919–924.

    CAS  Google Scholar 

  • Ogato, G. S., Bantider, A., Abebe, K., & Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West Shoa Zone, Oromia Regional State. Ethiopia. Journal of Hydrology: Regional Studies, 27, 100659. https://doi.org/10.1016/j.ejrh.2019.100659

    Article  Google Scholar 

  • Panhalkar, S.S., and Jarag, A.P. (2017). Flood risk assessment of Panchganga River (Kolhapur district, Maharashtra) using GIS-based multicriteria decision technique. Current Science, 112(4):785–793, http://www.jstor.org/stable/24912579

  • Patel, D. P., Ramirez, J. A., Srivastava, P. K., Bray, M., & Han, D. (2017). Assessment of flood inundation map** of Surat city by coupled 1D/2D hydrodynamic modeling: A case application of the new HEC-RAS 5. Natural Hazards, 89, 93–130. https://doi.org/10.1007/s11069-017-2956-6

    Article  Google Scholar 

  • Pathan, A. I., Girish Agnihotri, P., Said, S., & Patel, D. (2022). AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat. India. Environmental Monitoring and Assessment, 194, 509. https://doi.org/10.1007/s10661-022-10111-x

    Article  Google Scholar 

  • Paul, P., & Sarkar, R. (2022). Flood susceptible surface detection using geospatial multi-criteria framework for management practices. Natural Hazards, 114, 3015–3041. https://doi.org/10.1007/s11069-022-05503-8

    Article  Google Scholar 

  • Pham, B. T., Luu, C., Van Phong, T., Nguyen, H. D., Van Le, H., Tran, T. Q., Ta, H. T., & Prakash, I. (2021). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province. Vietnam. Journal of Hydrology, 592, 125815. https://doi.org/10.1016/j.jhydrol.2020.125815

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Rao, K. H. V. D., Alladi, S., & Singh, A. (2019). An integrated approach in develo** flood vulnerability index of India using spatial multi-criteria evaluation technique. Current Science, 117(1), 80–86. https://doi.org/10.18520/cs/v117/i1/80-86

    Article  Google Scholar 

  • Rastogi, M., Chauhan, A., Vaish, R., & Kishan, A. (2015). Selection and performance assessment of phase change materials for heating, ventilation and air-conditioning applications. Energy Conversion and Management, 89, 260–269. https://doi.org/10.1016/j.enconman.2014.09.077

    Article  Google Scholar 

  • Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002

    Article  Google Scholar 

  • Roy, D. C., & Blaschke, T. (2015). Spatial vulnerability assessment of foods in the coastal regions of Bangladesh. Geomatics, Natural Hazards and Risk, 6(1), 21–44. https://doi.org/10.1080/19475705.2013.816785

    Article  Google Scholar 

  • Saaty, T. L. & Vargas, G. L. (2001). Models, methods, concepts, and applications of the analytic hierarchy process. Part of the book series: International Series in Operations Research & Management Science, (pp. 346). Springer New York. https://doi.org/10.1007/978-1-4615-1665-1

  • Saaty, T.L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill Company. New York, NY, 27 p.

  • Samanta, S., Koloa, C., Kumar Pal, D., & Palsamanta, B. (2016). Flood risk analysis in lower part of Markham river based on Multi-Criteria Decision Approach (MCDA). Hydrology, 3(3), 29. https://doi.org/10.3390/hydrology3030029

    Article  Google Scholar 

  • Sarkar, D., & Mondal, P. (2020). Flood vulnerability map** using frequency ratio (FR) model: A case study on Kulik river basin, Indo Bangladesh Barind region. Applied Water Science, 10(1), 17. https://doi.org/10.1007/s13201-019-1102-x

    Article  Google Scholar 

  • Shafapour-Tehrany, M., Shabani, F., NeamahJebur, M., Hong, H., Pourghasemi, H. R., & **e, X. (2017). GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk, 8(2), 1538–1561. https://doi.org/10.1080/19475705.2017.1362038

    Article  Google Scholar 

  • Sharma, A., Wasko, C., & Lettenmaier, D. P. (2018). If precipitation extremes are increasing, why aren’t floods? Water Resources Research, 54(11), 8545–8551. https://doi.org/10.1029/2018WR023749

    Article  Google Scholar 

  • Singha, C., & Swain, K. C. (2016). Land suitability evaluation criteria for agricultural crop selection: A review. Agricultural Reviews, 37(2), 125–132.

    Article  Google Scholar 

  • Sinha, R., Bapalu, G. V., Singh, L. K., & Rath, B. (2008). Flood risk analysis in the Kosi river basin, using multiparametric approach of analytical hierarchy process (AHP). Journal of the Indian Society of Remote Sensing, 36, 335–349. https://doi.org/10.1007/s12524-008-0034-y

    Article  Google Scholar 

  • Sinha, A.K. (2014). District groundwater brochure Patan District Gujarat: Technical report series. Central Ground Water Board, Ministry of Water Resources, Government of India, 27 p.

  • Socaciu, L., Giurgiu, O., Banyai, D., & Simion, M. (2016). PCM selection using AHP method to maintain thermal comfort of the vehicle occupants. Energy Procedia, 85, 489–497. https://doi.org/10.1016/j.egypro.2015.12.232

    Article  Google Scholar 

  • Stewart, B., Woolhiser, D., Wischmeier, W., Caro, J., & Frere, M. H. (1975). Control of water pollution from cropland (p. 326). Office of research and development environmental protection agency, Department of Agriculture, Agricultural Research Service.

    Google Scholar 

  • Stoleriu, C. C., Urzica, A., & Mihu-Pintilie, A. (2020). Improving flood risk map accuracy using high-density LiDAR data and the HEC-RAS river analysis system: A case study from north-eastern Romania. Journal of Flood Risk Management, 13(Suppl. 1), e12572. https://doi.org/10.1111/jfr3.12572

    Article  Google Scholar 

  • Subrahmanyam, V. P. (1986). Hazards of floods and droughts in India. In M. I. El-Sabh & T. S. Murty (Eds.), Natural and man-made hazards (pp. 337–356). D. Reidel Publishing Company.

    Google Scholar 

  • Sugianto, S., Deli, A., Miswar, E., Rusdi, M., & Irham, M. (2022). The effect of land use and land cover changes on flood occurrence in Teunom Watershed. Aceh Jaya. Land, 11(8), 1271. https://doi.org/10.3390/land11081271

    Article  Google Scholar 

  • Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility map** through the GIS-AHP technique using the clou. ISPRS International Journal of Geo-Information, 9(720), 3–23. https://doi.org/10.3390/ijgi9120720

    Article  Google Scholar 

  • Tehrany, M. S., & Kumar, L. (2018). The application of a Dempster–Shafer-based evidential belief function in flood susceptibility map** and comparison with frequency ratio and logistic regression methods. Environmental Earth Sciences, 77, 490. https://doi.org/10.1007/s12665-018-7667-0

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of food 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

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility map** using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008

    Article  Google Scholar 

  • Tehrany, M. S., Shabani, F., Jebur, M. N., Hong, H., Chen, W., & **e, X. (2017). GIS-based spatial prediction of food prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk, 8(2), 1538–1561. https://doi.org/10.1080/19475705.2017.1362038

    Article  Google Scholar 

  • UNFCCC, 2022. Draft decision entitled “Sharm el-Sheikh Implementation Plan” proposed under agenda item 2 of the Conference of the Parties at its twenty-seventh session-November 2022: Sessional proceedings, Pp. 1–13, https://unfccc.int/documents/624441

  • Vignesh, K. S., & Madha Suresh, V. (2018). An assessment of food vulnerability using risk matrix method- a case study of Kanayakumari district. Tamil Nadu. Journal of Global Resources, 4(01), 102–106.

    Google Scholar 

  • Vogel, R. M., Rosner, A., & Kirshen, P. H. (2013). Brief communication: Likelihood of societal preparedness for global change: Trend detection. Natural Hazards and Earth System Sciences, 13, 1773–1778. https://doi.org/10.5194/nhess-13-1773-2013

    Article  Google Scholar 

  • Vojtek, M., & Vojteková, J. (2019). Flood susceptibility map** on a national scale in slovakia using the analytical hierarchy process. Water, 11(2), 364. https://doi.org/10.3390/w11020364

    Article  Google Scholar 

  • Yang, X. L., Ding, J. H., & Hou, H. (2013). Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis. Natural Hazards, 68, 657–674. https://doi.org/10.1007/s11069-013-0642-x

    Article  Google Scholar 

  • Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2018). Map** flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment, 615, 1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037

    Article  CAS  Google Scholar 

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Acknowledgements

The Integrated Nutrient Management Division, Department of Agriculture & Farmers Welfare, Ministry of Agriculture & Farmers Welfare, Government of India, is to be thanked by the authors for providing all the facilities and resources needed to conduct this study.

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Nitin Surendra Singh Gahalod contributed to the investigation, resources, formal analysis, file validation, data curation, supervision, project administration, and writing—preparation of the first draft; Kumar Rajeev, Pawan Kumar Pant, Sonam Binjola, and Rameshwar Lal Yadav made contributions to validation, software, formal analysis, resources, and writing (review and editing); Rang Lal Meena contributed to resources, project administration, supervision, and visualization. The final manuscript was read and approved by all authors.

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Correspondence to Nitin Surendra Singh Gahalod.

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Gahalod, N.S.S., Rajeev, K., Pant, P.K. et al. Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique—a case study of Patan district Gujarat, India. Environ Monit Assess 196, 338 (2024). https://doi.org/10.1007/s10661-024-12482-9

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