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Flood Susceptibility Analysis in Chennai Corporation Using Frequency Ratio Model

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Abstract

Natural disasters like flood are causing massive damages to natural and human resources, especially in coastal areas. In respect to social, economic and environmental perspective, flood is one of the most devastating disasters in Chennai for the recent days. Flood susceptibility mapping using frequency ratio model was done for the 88 micro watersheds of Adyar, Cooum and Kosasthalaiyar watersheds of Chennai Corporation area. Flood Susceptibility map was generated using frequency ratio model by considering ten different independent variables (landuse/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage) through weighted-based bivariate probability values. In total, 123 historic flood reported locations were taken for this study from which 100 locations were used for susceptibility mapping and 23 locations were used for validation. Both the independent variables and historic flood locations were combined together to generate frequency ratio database for flood susceptibility mapping. The developed frequency ratio was varied from 0 to 27.11 and reclassified into five flood vulnerability zones namely, very low (less than 5.0), low (5.0 7.5), moderate (7.5–10.0), high (10.0–12.5) and very high susceptibility (more than 12.5). The result revealed that 10.48 and 38.93 percentage of the land have very high and high vulnerable class, respectively. The frequency ratio model validated using 23 flood locations, where 22 locations are presented in high and very high susceptibility class. This analysis exemplified that prediction was with success rate of 95.6%. The flood susceptibility analysis using this model will be very useful and efficient tool to the local government administrators, researchers and planners for devising flood mitigation plans.

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Abbreviations

FR:

Frequency Ratio

RS:

Remote Sensing

GIS:

Geographic Information System

TWI:

Topographic Wetness Index

NRSC:

National Remote Sensing Centre

SCS:

Soil Conservation Service

AMC:

Antecedent soil Moisture Condition

AHP:

Analytical Hierarchy Process

MCDA:

Multi-Criteria Decision support Approach

WofE:

Weights of Evidence

ANN:

Artificial Neural Networks

NRSC:

National Remote Sensing Centre

SRTM:

Shuttle Rader Topographic mission

References

  • Adiat, K. A. N., Nawawi, M. N. M., & Abdullah, K. (2012). Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool–a case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology, 440–41, 75–89.

    Article  Google Scholar 

  • Amutha, R., & Porchelvan, P. (2009). Estimation of surface runoff in malattar sub-watershed using SCS-CN method. Journal of Indian Society of Remote Sensing, 37(2), 291–304.

    Article  Google Scholar 

  • BEVEN, K. J., & KIRKBY, M. J. (1979). A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24, 43–69.

    Article  Google Scholar 

  • Bhatt, C. M., & Mishra, A. (2016). Chennai floods, 2015 (a satellite and field based assessment study). Report of Disaster Management Support Division.

    Google Scholar 

  • Billa, L., Shattri, M., Mahmud, A. R., & Ghazali, A. H. (2006). Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prevention and Management, 15, 233–240.

    Article  Google Scholar 

  • Bisht, D. S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., & Panda, A. (2016). Modeling urban floods and drainage using SWMM and MIKE URBAN: A case study. Natural Hazards, 84(2), 749–776.

    Article  Google Scholar 

  • Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: modeling with GIS. In F. Bonham-Carter (Ed.), Computer methods in the geosciences. Oxford: Pergamon press.

    Google Scholar 

  • Qin, C.-Z., Zhu, A.-X., Pei, T., Li, B.-L., Scholten, T., Behrens, T., & Zhou, C.-H. (2011). An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agriculture, 12, 32–43.

    Article  Google Scholar 

  • Chen, Y. R., Yeh, C. H., & Yu, B. (2011). Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Natural Hazards, 59(3), 1261–1276.

    Article  Google Scholar 

  • Faiz-Ahmed, C., & Kranthi, N. (2018). Flood vulnerability assessment using geospatial techniques Chennai India. Indian Journal of Science and Technology., 11(6), 1–13.

    Article  Google Scholar 

  • Haghizadeh, A., Siahkamari, S., Haghiabi, A. H., & 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 

  • Huang, X., Tan, H., Zhou, J., Yang, T., Benjamin, A., Wen, S. W., Li, S., Liu, A., Li, X., Fen, S., & Li, X. (2008). Flood hazard in Hunan province of China: an economic loss analysis. Natural Hazards, 47, 65–73.

    Article  Google Scholar 

  • Jameson, S., & Baud, I. (2016). Varieties of knowledge for assembling an urban flood management governance configuration in Chennai India. Habitat International, 54, 112–123.

    Article  Google Scholar 

  • Jena, P. P., Panigrahi, B., & Chatterjee, C. (2016). Assessment of Cartosat-1 DEM for modeling floods in data scarce regions. Water Resources Management, 30(3), 1293–1309.

    Article  Google Scholar 

  • Kumar, P., Tiwari, K. N., & Pal, D. K. (1991). Establishing SCS runoff curve number from IRS digital database. Journal of the Indian Society of Remote Sensing, 19(4), 245–251.

    Article  Google Scholar 

  • Lavanya, A. K. (2012). Urban Flood Management – A case study of Chennai city. Architecture Research, 2(6), 115–121.

    Article  Google Scholar 

  • Lee, M.J., Kang, J.E., & Jeon, S. (2012). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: Proceedings of Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. Munich, 895–898.

  • Liao, X., & Carin, L. (2009). Migratory logistic regression for learning concept drift between two data sets with application to UXO sensing. IEEE Transactions on Geoscience and Remote Sensing, 47, 1454–1466.

    Article  Google Scholar 

  • Lohani, A., Kumar, R., & Singh, R. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442, 23–35.

    Article  Google Scholar 

  • Manandhar, B. (2010). Flood plain analysis and risk assessment of Lothar Khola. MSc Thesis, Tribhuvan University, Phokara, Nepal, pp. 64.

  • McGranahan, G., Balk, D., & Anderson, B. (2007). The rising tide assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization, 19(1), 17–37.

    Article  Google Scholar 

  • NDMP. (2016). A publication of the national disaster management authority. Government of India.

    Google Scholar 

  • Pal, B., & Samanta, S. (2011). Surface runoff estimation and mapping using remote sensing and geographic information system. International Journal of Advanced Science and Technology, 3(2), 106–114.

    Google Scholar 

  • Pradhan, B., & Buchroithner, M. F. (2010). Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environmental and Engineering Geoscience, 16, 107–126.

    Article  Google Scholar 

  • Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2016). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province Iran. Geocarto International. https://doi.org/10.1080/10106049.2015.1041559

    Article  Google Scholar 

  • Rao, K. V., Bhattacharya, A. K., & Mishra, K. (1996). Runoff estimation by curve number method-case studies. Journal of Soil and Water Conservation, 40, 1–7.

    Google Scholar 

  • Regmi, N. R., Giardino, J. R., & Vitek, J. D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115, 172–187.

    Article  Google Scholar 

  • Sahoo, D. P., Sahoo, B., & Tiwari, M. K. (2020). Copula-based probabilistic spectral algorithms for high-frequent streamflow estimation. Remote Sensing of Environment, 251, 112092.

    Article  Google Scholar 

  • Samanta, S., Pal, D. K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing GIS and frequency ratio model. Applied Water Science, 8, 66.

    Article  Google Scholar 

  • Samanta, S., Pal, D. K., Lohar, D., & Pal, B. (2012). Interpolation of climate variables and temperature modeling. Theoretical and Applied Climatolgy, 107(1), 35–45. https://doi.org/10.1007/s00704-011-0455-3

    Article  Google Scholar 

  • 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), 29. https://doi.org/10.3390/hydrology3030029

    Article  Google Scholar 

  • Sezer, E. A., Pradhan, B., & Gokceoglu, C. (2011). Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley Malaysia. Expert Systems with Applications, 38(7), 8208–8219.

    Article  Google Scholar 

  • SCS. (1972). Soil conservation department. Handbook of Hydrology.

    Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 29, 1149–1165. https://doi.org/10.1007/s00477-015-1021-9

    Article  Google Scholar 

  • Tiwari, M. K., & Chatterjee, C. (2010). Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). Journal of Hydrology, 382(1), 20–33.

    Article  Google Scholar 

  • WHO (2003). World Health Organization. Disaster data-key trends and statistics in World Disasters Report; WHO: Geneva, Switzerland. http://www.ifrc.org/PageF iles/89755/2003/43800 -WDR2003_En.pdf

  • Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA, 72, 1–12.

    Article  Google Scholar 

  • Youssef, A. M., Pradhan, B., & Hassan, A. M. (2011). Flash flood risk estimation along the St.Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences, 62, 611–662.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are very grateful to National Centre for Coastal Research (NCCR) of the Ministry of Earth Science (MoES, India) for the financial support and continuous encouragement.

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Authors and Affiliations

  1. Centre for Disaster Management and Coastal Research, Department of Remote Sensing, Bharathidasan University, Tiruchirappalli, Tamilnadu, 620023, India

    Logesh Natarajan, Bavinaya Palpanabhan Kasthuri, Prabhakaran Moorthy & Lakshumanan Chokkalingam

  2. National Centre for Coastal Research, Ministry of Earth Sciences, Government of India, Chennai, 600100, India

    Tune Usha

  3. French Institute of Pondicherry, Puducherry, 605001, India

    Muthusankar Gowrappan

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  1. Logesh Natarajan
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  2. Tune Usha
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  3. Muthusankar Gowrappan
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  4. Bavinaya Palpanabhan Kasthuri
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  5. Prabhakaran Moorthy
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  6. Lakshumanan Chokkalingam
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Correspondence to Logesh Natarajan.

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Natarajan, L., Usha, T., Gowrappan, M. et al. Flood Susceptibility Analysis in Chennai Corporation Using Frequency Ratio Model. J Indian Soc Remote Sens 49, 1533–1543 (2021). https://doi.org/10.1007/s12524-021-01331-8

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  • DOI: https://doi.org/10.1007/s12524-021-01331-8

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