Skip to main content

Advertisement

SpringerLink
Log in

Urban flood susceptibility analysis of Saroor Nagar Watershed of India using Geomatics-based multi-criteria analysis framework

  • Environmental Impacts and Consequences of Urban Sprawl
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

During the monsoon season, flooding is common in several parts of India, and urban areas are becoming more prone to flooding even during less intense rainfall events as a result of the encroachment of waterbodies and natural drainage channels, increased impervious areas, and subsequent decrease in infiltration capabilities. In the years 2000, 2008, 2016, 2019, 2020, and 2021, severe flood events in our study area, the Saroor Nagar urban watershed in Telangana state, caused human loss, economic devastation, and environmental devastation. Although flood risk cannot be completely eliminated, it can be significantly reduced by developing a flood hazard model to identify flood-prone areas in a watershed, which can assist decision makers seeking comprehensive flood risk management. The flood hazard map is created by integrating Geomatics with multi-criteria decision analysis and the analytical hierarchy process (AHP). Topographic wetness index (TWI), digital elevation model (DEM), slope, precipitation, drainage density, distance to waterbody, soil, and land use land cover (LULC) were the flooding causative elements considered in this study. The resulting flood risk map is divided into four distinct categories that reflect flood danger levels of low, moderate, high, and extremely high. The flood risk map revealed that the moderate risk zone decreased from 50.2 to 45.7% of the study area between 2008 and 2020, while the high-risk zone increased from 45.2 to 52.8%. The flood susceptibility map is validated using crowdsourcing techniques. The findings demonstrated that a Geographic Information System (GIS)-based multi-criteria analysis framework for flood hazard analysis could be effectively used to aid disaster management decision-making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

References

  • Abebe Y, Kabir G, Tesfamariam S (2018) Assessing urban areas vulnerability to pluvial fooding using GIS applications and Bayesian belief network model. J Clean Prod 174:1629–1641. https://doi.org/10.1016/j.jclepro.2017.11.066http://www.sciencedirect.com/science/article/pii/S0959652617327245

    Article  Google Scholar 

  • Awawdeh M, Obeidat M, Al-Mohammad M, Al-Qudah K, Jaradat R (2014) Integrated GIS and remote sensing for mapping groundwater potentiality in the Tulul al Ashaqif. Arab J Geosci 7(6):2377–2392

    Article  CAS  Google Scholar 

  • Beltramone G, Alaniz E, Ferral AE, Aleksink A, Arijn DR, Bernasconi I, Germn A, Ferral A (2017) Risk mapping of urban areas prone to flash floods in mountain basins using the analytic hierarchy process and geographical information systems. In: 2017 XVII Workshop on information processing and control (RPIC), 1–6. https://doi.org/10.23919/RPIC.2017.8214379

  • Bisht S, Chaudhry S, Sharma S, Soni S (2018) Assessment of flash food vulnerability zonation through Geospatial technique in high altitude Himalayan watershed, Himachal Pradesh India. Remote Sens Appl: Soc Environ 12:35–47. https://doi.org/10.1016/j.rsase.2018.09.001

    Article  Google Scholar 

  • CetinkayaZceylanErbaKabak CEMM (2016) GIS-based fuzzy MCDA approach for siting refugee camp: a case study for south eastern Turkey. Int J Disaster Risk Reduct 18:218–231

    Article  Google Scholar 

  • 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. Nat Hazards 99(1):247–274

    Article  Google Scholar 

  • Chen H, Ito Y, Sawamukai M, Tokunaga T (2015) Flood hazard assessment in the Kujukuri plain of Chiba prefecture, Japan, based on GIS and multicriteria decision analysis. Nat Hazards 78(1):105–120. https://doi.org/10.1007/s11069-015-1699-5

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  • Dash P, Sar J (2020) Identification and validation of potential flood hazard area using GIS based multi-criteria analysis and satellite data-derived water index. J Flood Risk Manag 13(3):e12620

    Article  Google Scholar 

  • Deng Z, Zhang X, Li D, Pan G (2015) Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin. Environ Earth Sci 73(3):1119–1132

    Article  Google Scholar 

  • Dou X, Song J, Wang L, Tang B, Xu S, Kong F, Jiang X (2018) Flood risk assessment and mapping based on a modified multi-parameter flood hazard index model in the Guanzhong Urban Area. Stoch Environ Res Risk Assess 32(4):1131–1146

    Article  Google Scholar 

  • EbrahimianGhajari Y, Alesheikh AA, Modiri M, Hosnavi R, Abbasi M, Sharifi A (2018) Urban vulnerability under various blast loading scenarios: analysis using GIS-based multi-criteria decision analysis techniques. Cities 72:102–114. https://doi.org/10.1016/j.cities.2017.08.006

    Article  Google Scholar 

  • Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). Egypt J Remote Sens Space Sci 18(2):261–278. https://doi.org/10.1016/j.ejrs.2015.06.007 (http://www.sciencedirect.com/science/article/pii/S1110982315000307)

    Article  Google Scholar 

  • Elsheikh RFA, Ouerghi S, Elhag AR (2015) Flood risk map based on GIS, and multi criteria techniques (case study Terengganu Malaysia). J Geogr Inf Syst 7(04):348–357

    Google Scholar 

  • Epicollect5 Data Collection User Guide. https://epicollect5.gitbooks.io/epicollect5-user-guide. Accessed 14 June 2022

  • Gambini JM, Laymito JA (2019) Multiparametric ahp-based flood hazard zonation approach in northwestern Peru at basin scale. E-Proceedings of the 38th IAHR World Congress, September 1-6, 2019, Panama City, Panama

  • Ghosh A, Kar S (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal. Nat Hazards 94(1):349–368

    Article  Google Scholar 

  • Goepel K (2013) Implementing the analytic hierarchy process as a standard method for multi-criteria decision making in corporate enterprises a new AHP excel template with multiple inputs. Proceedings of the International Symposium on the Analytic Hierarchy Process, Kuala Lumpur. https://doi.org/10.13033/isahp.y2013.047

    Book  Google Scholar 

  • Hadi Allafta and Christian Opp (2021) GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran. Geomatics Nat Hazard Risk 12(1):2087–2116. https://doi.org/10.1080/19475705.2021.1955755

    Article  Google Scholar 

  • 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). Arab J Geosci 12(21). https://doi.org/10.1007/s12517-019-4754-9.

  • Islam M, Sado K (2000) Flood hazard assessment for the construction of flood hazard map and land development priority map using NOAA/AVHRR data and GIS–a case study in Bangladesh. Hydrol Sci J Des Sci Hydrol 45(3):337–357

    Article  Google Scholar 

  • Lee MJ, Kang JE, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. 895–898. Paper presented at 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, Germany. https://doi.org/10.1109/IGARSS.2012.6351414

  • Lei W, Dong H, Chen P, Lv H, Fan L, Mei G (2020) Study on runoff and infiltration for expansive soil slopes in simulated rainfall. Water 12(1):222

    Article  Google Scholar 

  • Liang Q, Xia X, Hou J (2016) Catchment-scale high-resolution flash flood simulation using the GPU-based technology. Procedia Eng 154:975–981. https://doi.org/10.1016/j.proeng.2016.07.585

    Article  Google Scholar 

  • Machiwal D, Rangi N, Sharma A (2015) Integrated knowledge- and data-driven approaches for groundwater potential zoning using GIS and multi-criteria decision-making techniques on hard-rock terrain of Ahar catchment, Rajasthan, India. Environ Earth Sci 73(4):1871–1892

    Article  Google Scholar 

  • Mahmoud S, Gan TY (2018) Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J Clean Prod 196:216–229. https://doi.org/10.1016/j.jclepro.2018.06.047

    Article  Google Scholar 

  • Mishra K, Sinha R (2020) Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: a hydro-geomorphic approach. Geomorphology 350:106861. https://doi.org/10.1016/j.geomorph.2019.106861

    Article  Google Scholar 

  • Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHB (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics Nat Hazard Risk 8:1080–1102

    Article  Google Scholar 

  • Munjuluri Kalyan, Pal Indrajit, Tripathi Nitin Kumar (2019) Geo-spatial techniques for rapid Post Disaster Needs Assessment (rPDNA). Int J Recent Technol Eng (IJRTE) 8(4):11198–11206. https://doi.org/10.35940/ijrte.D8017.118419. ISSN: 2277–3878 (Online)

    Article  Google Scholar 

  • Nott J (2006) Extreme events: a physical reconstruction and risk assessment. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Nyarko BK (2002) Application of a rational model in GIS for flood risk assessment in Accra. Ghana J Spat Hydrol 2:1–14

    Google Scholar 

  • Ogato G, 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. J Hydrol Regional Studies 27:100659

    Article  Google Scholar 

  • Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6(6):1515–1545. https://doi.org/10.3390/w6061515

  • Papaioannou G, Vasiliades L, Loukas A (2015) Multi-criteria analysis framework for potential flood prone areas mapping. Water Resour Manag 29(2):399–418. https://doi.org/10.1007/s11269-014-0817-6

    Article  Google Scholar 

  • Poussin JK, Botzen WW, Aerts JC (2014) Factors of influence on flood damage mitigation behavior by households. Environ Sci Pol 40:69–77 Journal (2019), Article ID 3814962, 7

    Article  Google Scholar 

  • Price K, Jackson CR, Parker AJ (2010) Variation of surficial soil hydraulic properties across land uses in the southern Blue Ridge Mountains, North Carolina, USA. J Hydrol 383(3–4):256–268

    Article  CAS  Google Scholar 

  • Quan RS, Liu M, Lu M, Zhang LJ, Wang JJ, Xu SY (2010) Waterlogging risk assessment based on land use/cover change: a case study in Pudong new area, shanghai. Environ Earth Sci 61(6):1113–1121. https://doi.org/10.1007/s12665-009-0431-8

    Article  Google Scholar 

  • Radmehr A, Araghinejad S (2015) Flood vulnerability analysis by fuzzy spatial multi criteria decision making. Water Resour Manage 29(12):4427–4445

    Article  Google Scholar 

  • Rahman M, Ningsheng C, Islam M, Dewan A, Iqbal J, Washakh R, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi criteria decision analysis. Earth Syst Environ 3(3):585–601

    Article  Google Scholar 

  • Rahmati O, Zeinivand H, Besharat M (2015) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Haz Risk 7(3):1000–1017. https://doi.org/10.1080/19475705.2015.1045043

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70

    Article  Google Scholar 

  • Rajasekhar M, Sudarsana Raju G, Sreenivasulu Y, Siddi Raju R (2019) Delineation of groundwater potential zones in semi-arid region of Jilledubanderu river basin, Anantapur District, Andhra Pradesh, India using fuzzy logic, AHP and integrated fuzzy-AHP approaches. HydroResearch 2:97–108. https://doi.org/10.1016/j.hydres.2019.11.006

    Article  Google Scholar 

  • RazaviTermeh SV, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451. https://doi.org/10.1016/j.scitotenv.2017.09.262

    Article  CAS  Google Scholar 

  • Riad P, Billib M, Hassan A, Salam M, El Din M (2011) Application of the overlay weighted model and boolean logic to determine the best locations for artificial recharge of groundwater. JUEE 5(2):57–66

    Article  Google Scholar 

  • Saaty T (1980) The analytic hierarchy process, xiii edn. McGraw-Hill International Book Co., New York

    Google Scholar 

  • Saaty T (1990) How to make a decision: the analytic hierarchy process? Eur J Oper Res 48(1):9–26

    Article  Google Scholar 

  • Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  • Saaty T (2014) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, Pittsburgh

    Google Scholar 

  • Sabzi HZ, Humberson D, Abudu S, King JP (2016) Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, southern new Mexico. Expert Syst Appl 43:154–164

    Article  Google Scholar 

  • Sadeghi-Pouya A, Nouri J, Mansouri N, Kia-Lashaki A (2017) An indexing approach to assess flood vulnerability in the western coastal cities of Mazandaran, Iran. Int J Disaster Risk Reduct 22:304–316. https://doi.org/10.1016/j.ijdrr.2017.02.013

    Article  Google Scholar 

  • Saha AK, Agrawal S (2020) Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnol Environ Eng 5:11. https://doi.org/10.1007/s41204-020-00073-1

    Article  CAS  Google Scholar 

  • Sahana M, Sajjad H (2019) Vulnerability to storm surge food using remote sensing and GIS techniques: a study on Sundarban Biosphere Reserve, India. Remote Sens Appl: Soc Environ 13:106–120. https://doi.org/10.1016/j.rsase.2018.10.008

    Article  Google Scholar 

  • Salleh S, Rahman AAA, Othman A, Mohd WNW (2018) Comparative study of landslides susceptibility mapping methods: multi-criteria decision making (MCDM) and artificial neural network (ANN). In: IOP conference series: earth and environmental science, IOP Publishing. https://doi.org/10.1088/1755-1315/117/1/012035

  • Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin. Model Earth Syst Environ 4(1):395–408

    Article  Google Scholar 

  • Sarker MZ, Sivertun Å (2011) GIS and RS combined analysis for flood prediction mapping– a case study of Dhaka City corporation. IJEP 1(3):30–42

    Article  Google Scholar 

  • Sauer VB (2002) USGS, the national flood frequency program, version 3: a computer program for estimating magnitude and frequency of floods for ungaged sites

  • Schmitt TG, Thomas M, Ettrich N (2004) Analysis and modeling of flooding in urban drainage systems. J Hydrol 299(3–4):300–311

    Article  Google Scholar 

  • Sheena AD, Ramalingam M, Anuradha B (2019) Remote sensing and GIS in the recent Chennai food study. Nanotechnol Environ Eng 4:3. https://doi.org/10.1007/s41204-018-0049-8

    Article  Google Scholar 

  • Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O (2018) Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int 33(9):927–941. https://doi.org/10.1080/10106049.2017.1316780

    Article  Google Scholar 

  • Subbarayan S, Sivaranjani S (2020) Modelling of flood susceptibility based on gis and analytical hierarchy process—a case study of Adayar River Basin, Tamilnadu, India. In: Pal I, von Meding J, Shrestha S, Ahmed I, Gajendran T (eds) An interdisciplinary approach for disaster resilience and sustainability. MRDRRE 2017. Disaster Risk Reduction (Methods, Approaches and Practices). Springer, Singapore

  • Subramanian N, Ramanathan R (2012) A review of applications of analytic hierarchy process in operations management. Int J Prod Econ 138(2):215–241

    Article  Google Scholar 

  • Tang X, Shu Y, Lian Y, Zhao Y, Fu Y (2018) A spatial assessment of urban waterlogging risk based on a weighted nave bayes classifier. Sci Total Environ 630:264–274. https://doi.org/10.1016/j.scitotenv.2018.02.172

    Article  CAS  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule-based decision tree (dt) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034

    Article  Google Scholar 

  • Tehrany MS, 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

    Article  Google Scholar 

  • Todini F, De Filippis T, De Chiara G, Maracchi G, Martina M, Todini E (2004) Using a GIS approach to asses flood hazard at national scale. In: Proceedings of the European Geosciences Union, 1st General Assembly, Nice, pp 25–30

  • Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, eastern Hindu Kush. Pakistan Plos One 15(3):e0229153

    Article  CAS  Google Scholar 

  • Vaddiraju SC, Reshma T (2022) Urbanization implications on hydro-meteorological parameters of Saroor Nagar Watershed of Telangana. Environ Challenges 8:100562. https://doi.org/10.1016/j.envc.2022.100562. ISSN 2667–0100

    Article  Google Scholar 

  • Vishnu CL, Sajinkumar KS, Oommen T et al (2019) Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomat Nat Hazards Risk 10:758–767. https://doi.org/10.1080/19475705.2018.1543212

    Article  Google Scholar 

  • Wang Y, Li Z, Tang Z, Zeng G (2011) A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake Region, Hunan, Central China. Water Resour Manag 25(13):3465–3484

    Article  Google Scholar 

  • Wu Y, Zhong P, Zhang Y, Xu B, Ma B, Yan K (2015) Integrated flood risk assessment and zonation method: a case study in Huaihe River basin. Nat Hazards 78(1):635–651

    Article  Google Scholar 

  • Xiao Y, Yi S, Tang Z (2017) Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Sci Total Environ 599–600(2017):1034–1046

    Article  Google Scholar 

  • Yan B, Fang NF, Zhang PC, Shi ZH (2013) Impacts of land use change on watershed streamflow and sediment yield: an assessment using hydrologic modelling and partial least squares regression. J Hydrol 484:26–37

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Zou Q, Zhou J, Zhou C, Song L, Guo J (2013) Comprehensive food risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Environ Res Risk Assess 27(2):525–546. https://doi.org/10.1007/s00477-012-0598-5

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Tadepalligudem, Andhra Pradesh, India

    Shiva Chandra Vaddiraju & Reshma Talari

  2. Department of Civil Engineering, Maturi Venkata Subba Rao Engineering College, Hyderabad, Telangana, India

    Shiva Chandra Vaddiraju

Authors
  1. Shiva Chandra Vaddiraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reshma Talari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Shiva Chandra Vaddiraju. The first draft of the manuscript was written by Shiva Chandra Vaddiraju, and Reshma Talari suggested improvements in the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shiva Chandra Vaddiraju.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Marcus Schulz

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaddiraju, S.C., Talari, R. Urban flood susceptibility analysis of Saroor Nagar Watershed of India using Geomatics-based multi-criteria analysis framework. Environ Sci Pollut Res 30, 107021–107040 (2023). https://doi.org/10.1007/s11356-022-24672-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24672-4

Keywords

Search

Navigation