Skip to main content

Advertisement

SpringerLink
Log in

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

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

Similar content being viewed by others

Data availability

The data will be available on request from the corresponding author.

References

  • Abu Alfeilat, H. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. B. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: A review. Big Data, 7(4), 221–248. https://doi.org/10.1089/big.2018.0175

    Article  PubMed  Google Scholar 

  • Agartala. (2019). Agartala becomes India’s third int’l internet gateway | Agartala News – Times of India. The Times of India.

  • Agartala. (2020). Agartala Climatological Table 1981–2010. India Meteorological Department.

  • Ahmed, I. A., Talukdar, S., Parvez, A., Rihan, M., Baig, M. R. I., & Rahman, A. (2022). Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms. Geocarto International. https://doi.org/10.1080/10106049.2022.2066200

  • Ahmed, I., Das, N., Debnath, J., & Bhowmik, M. (2017). An assessment to prioritise the critical erosion-prone sub-watersheds for soil conservation in the Gumti basin of Tripura, North-East India. Environmental Monitoring and Assessment, 189, 1–15.

  • Altaf, F., Meraj, G., & Romshoo, S. A. (2013). Morphometric analysis to infer hydrological behaviour of Lidder watershed, Western Himalaya India. Geography Journal, 2013.

  • Altaf, S., Meraj, G., & Romshoo, S. A. (2014). Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed. Environmental Monitoring and Assessment, 186, 8391–8412.

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Bhowmik, M., & Das, N. (2015). Flood hazard: A case study of the Lohar Nala, West Tripura District. Emerging Environmental Issues. In D. Sarkar & N. Gupta (Eds.), Today’s Society with Special Reference to North East India, Proceedings (pp. 53–66). Supriya Pub.

    Google Scholar 

  • Carter, J. G., Cavan, G., Connelly, A., Guy, S., Handley, J., & Kazmierczak, A. (2015). Climate change and the city: Building capacity for urban adaptation. Progress in Planning, 95, 1–66. https://doi.org/10.1016/j.progress.2013.08.001

    Article  Google Scholar 

  • Chakrabortty, R., Pal, S. C., Ruidas, D., Roy, P., Saha, A., & Chowdhuri, I. (2023). Living with floods using state-of-the-art and geospatial techniques: flood mitigation alternatives, management measures, and policy recommendations. Water, 15(3), 558.

    Article  Google Scholar 

  • Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modeling & Software, 95, 229–245.

    Article  Google Scholar 

  • Chau, K., Wu, C., & Li, Y. (2005). Comparison of several flood forecasting models in Yangtze River. Journal of Hydraulogical Engineering, 10(6), 485–491.

    MathSciNet  Google Scholar 

  • Chen, A., Djordjević, S., Leandro, J., et al. (2008). Simulation of the building blockage effect in urban flood modelling. In Conference: 11th International Conference on Urban Drainage.

    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, 2087–2096. https://doi.org/10.1016/J.SCITOTENV.2018.10.064

    Article  ADS  CAS  PubMed  Google Scholar 

  • Choudhury, S., Basak, A., Biswas, S., & Das, J. (2022). Flash flood susceptibility mapping using GIS-based AHP method. In Spatial modelling of flood risk and flood hazards: Societal implications (pp. 119–142). Springer International Publishing.

    Chapter  Google Scholar 

  • Choudhury, U., Singh, S. K., Kumar, A., Meraj, G., Kumar, P., & Kanga, S. (2023). Assessing land use/land cover changes and urban heat island intensification: A case study of Kamrup Metropolitan District, Northeast India (2000–2032). Earth, 4(3), 503–521.

    Article  ADS  Google Scholar 

  • Chowdhuri, I., Pal, S. C., Roy, P., Chakrabortty, R., Saha, A., & Shit, M. (2023). Evaluating the impact of climate change and geo-environmental factors on flood hazards in India: An integrated framework. Geological Journal. https://doi.org/10.1002/gj.4729

  • Cools, J., Vanderkimpen, P., El Afandi, G., Abdelkhalek, A., Fockedey, S., El Sammany, M., & Huygens, M. (2012). An early warning system for flash floods in hyper-arid Egypt. Natural Hazards and Earth System Sciences, 12(2), 443–457. https://doi.org/10.5194/nhess-12-443-2012

    Article  ADS  Google Scholar 

  • Costache, R., & Bui, D. T. (2019). Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Science of The Total Environment, 691, 1098–1118. https://doi.org/10.1016/J.SCITOTENV.2019.07.197

    Article  ADS  CAS  PubMed  Google Scholar 

  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964

    Article  Google Scholar 

  • Cui, H., Quan, H., Jin, R., & Lin, Z. (2023). Flood susceptibility mapping using novel hybrid approach of neural network with genetic quantum ensembles. KSCE Journal of Civil Engineering, 27(1), 431–441. https://doi.org/10.1007/s12205-022-0559-6

    Article  Google Scholar 

  • Dang, N. M., Babel, M. S., & Luong, H. T. (2011). Evaluation of food risk parameters in the day river flood diversion area, Red River delta Vietnam. Natural Hazards, 56(1), 169–194.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Das, S., & Gupta, A. (2021). Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin India. Geoscience Frontiers, 12(5), 101206.

    Article  Google Scholar 

  • Das, S., Ghosh, S., & Kayal, J. R. (2019). Liquefaction potential of Agartala City in Northeast India using a GIS platform. Bulletin of Engineering Geology and the Environment, 78, 2919–2931.

    Article  Google Scholar 

  • Debnath, J., Sahariah, D., Lahon, D., Nath, N., Chand, K., Meraj, G., Farooq, M., Kumar, P., Kanga, S., & Singh, S. K. (2022). Geospatial modeling to assess the past and future land use-land cover changes in the Brahmaputra Valley, NE India, for sustainable land resource management. Environmental Science and Pollution Research, 1–24.

  • Debnath, J., Pan, N. D., Ahmed, I., & Bhowmik, M. (2017). Channel migration and its impact on land use/land cover using RS and GIS: A study on Khowai River of Tripura, North-East India. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 197–210.

    Article  Google Scholar 

  • Debnath, J., Sahariah, D., Lahon, D., Nath, N., Chand, K., Meraj, G., Kumar, P., Singh, S. K., Kanga, S., & Farooq, M. (2023). Assessing the impacts of current and future changes of the planforms of river Brahmaputra on its land use-land cover. Geoscience Frontiers, 14(4), 101557.

    Article  Google Scholar 

  • Diakakis, M., Pallikarakis, A., & Katsetsiadou, K. (2014). Using a Spatio-Temporal gis database to monitor the spatial evolution of urban flooding phenomena. The case of anget metropolitan area in anget. ISPRS International Journal of Geo-Information, 3(1), 96-109.

  • Dinesh Kumar, P. K., Gopinath, G., & Seralathan, P. (2007). Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India. International Journal of Remote Sensing, 28(4), 5583–5601.

    Article  ADS  Google Scholar 

  • Douglas, I. (2017). Flooding in African cities, scales of causes, teleconnections, risks, vulnerability and impacts. International Journal Of Disaster Risk Reduction, 26, 34–42.

    Article  Google Scholar 

  • Edamo, M. L., Ukumo, T. Y., Lohani, T. K., Ayana, M. T., Ayele, M. A., Mada, Z. M., & Abdi, D. M. (2022). A comparative assessment of multi-criteria decision-making analysis and machine learning methods for flood susceptibility mapping and socio-economic impacts on flood risk in Abela-Abaya floodplain of Ethiopia. Environmental Challenges, 9, 100629.

    Article  Google Scholar 

  • El-Magd, S. A. (2022). Random forest and naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast Egypt. Arabian Journal of Geosciences, 15(3), 1–12.

    Google Scholar 

  • Emanuelsso, M. A. E., Mcintyre, N., Hunt, C. F., Mawle, R., Kitson, J., & Voulvoulis, N. (2014). Flood risk assessment for infrastructure networks. Journal of Flood Risk Management, 7(1), 31–41.

    Article  Google Scholar 

  • Extremes of temperature & rainfall for Indian stations (Up to 2012) (p. M209). India Meteorological Department. December 2016. Archived from the original (PDF) on 5 February 2020.

  • Fayaz, M., Meraj, G., Khader, S. A., Farooq, M., Kanga, S., Singh, S. K., Kumar, P., & Sahu, N. (2022). Management of landslides in a rural–urban transition zone using machine learning algorithms—A case study of a National Highway (NH-44) , India, in the Rugged Himalayan Terrains. Land, 11(6), 884.

    Article  Google Scholar 

  • Feng, B., Zhang, Y., & Bourke, R. (2021). Urbanization impacts on flood risks based on urban growth data and coupled flood models. Natural Hazards, 106, 613–627. https://doi.org/10.1007/s11069-020-04480-0

    Article  Google Scholar 

  • Ferreira, T. M., & Lourenço, P. B. (2019). Disaster risk reduction and urban resilience: concepts, methods and applications. Resilient Structures And Infrastructure, 453-473. https://doi.org/10.1007/978-981-13-7446-3_17

  • Ganjirad, M., & Delavar, M. R. (2023). Flood risk mapping using random forest and support vector machine. In ISPRS annals of the photogrammetry remote sensing and spatial information sciences (Vol X-4/W1-2022, pp. 201–208). https://doi.org/10.5194/isprs-annals-X-4-W1-2022-201-2023

  • Ghosh, A., Dey, P., & Ghosh, T. (2022). Integration of RS-GIS with frequency ratio, fuzzy logic, logistic regression and decision tree models for flood susceptibility prediction in lower angetic plain: A study on malda district of West Bengal, India. Journal of the Indian Society of Remote Sensing, 50(9), 1725–1745. https://doi.org/10.1007/s12524-022-01560-5

    Article  Google Scholar 

  • Gupta, K. (2007). Urban flood resilience planning and management and lessons for the future: a case study of Mumbai. India. Urban Water Journal, 4(3), 183–194.

    Article  Google Scholar 

  • Gupta, K. (2020). Challenges in developing urban flood resilience in India. Philosophical Transactions of the Royal Society A, 378(2168), 20190211.

    Article  ADS  Google Scholar 

  • Gupta, L., & Dixit, J. (2022). A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level. Geocarto International, 37(26), 11867–11899. https://doi.org/10.1080/10106049.2022.2060329

    Article  ADS  Google Scholar 

  • Haider, S., Paquier, A., Morel, R., Champagne, J.Y. (2003). Urban floodmodelling using computational fluid dynamics. In Proceedings of theInstitution of Civil Engineers-Water and Maritime Engineering 156(2), 129 – 135.

    Google Scholar 

  • Hansson, K., Danielson, M., & Ekenberg, L. (2008). A framework for evaluation of flood management strategies. Journal of environmental management, 86(3), 465–480.

    Article  CAS  PubMed  Google Scholar 

  • Hasanuzzaman, M., Adhikary, P. P., Bera, B., & Shit, P. K. (2022). Flood vulnerability assessment using AHP and frequency ratio techniques. In Spatial modelling of flood risk and flood hazards: societal implications (pp. 91–104). Springer International Publishing. https://doi.org/10.1007/978-3-030-94544-26

    Chapter  Google Scholar 

  • Islam, S., Tahir, M., & Parveen, S. (2022). GIS-based flood susceptibility mapping of the lower Bagmati basin in Bihar, using Shannon’s entropy model. Modeling Earth Systems and Environment, 1-15. https://doi.org/10.1007/s40808-021-01283-5

  • Janizadeh, S., Pal, S. C., Saha, A., Chowdhuri, I., Ahmadi, K., Mirzaei, S., et al. (2021). Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management, 298, 113551.

    Article  PubMed  Google Scholar 

  • Jhajharia, D., Roy, S., & Dhiman, S. D. (2006). Rainfall characteristics at AMA city (West Tripura). Journal of Engineering and Technology, Sardar PateI University, 19, 11–18.

    Google Scholar 

  • Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266

    Article  ADS  CAS  PubMed  Google Scholar 

  • Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., et al. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311–323.

    Article  ADS  Google Scholar 

  • Kumar, N., Liu, X., Narayanasamydamodaran, S., & Pandey, K. K. (2021). A systematic review comparing urban flood management practices in India to China’s sponge city program. Sustainability, 13(11), 6346.

    Article  Google Scholar 

  • Lawal, Z. K., Yassin, H., & Zakari, R. Y. (2021). Flood prediction using machine learning models: a case study of Kebbi State Nigeria. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1–6). https://doi.org/10.1109/CSDE53843.2021.9718497

    Chapter  Google Scholar 

  • Li, H., Wigmosta, M. S., Wu, H., Huang, M., Ke, Y., Coleman, A. M., & Leung, L. R. (2013). A physically based runoff routing model for land surface and earth system models. Journal of Hydrometeorology, 14, 808–828. https://doi.org/10.1175/JHM-D-12-015.1

  • Li, Y., & Hong, H. (2023). Modelling flood susceptibility based on deep learning coupling with ensemble learning models. Journal of Environmental Management, 325, 116450. https://doi.org/10.1016/j.jenvman.2022.116450

    Article  PubMed  Google Scholar 

  • Liu, S., Liu, R., & Tan, N. (2021a). A spatial improved-knn-based flood inundation risk framework for urban tourism under two rainfall scenarios. Sustainability, 13(5), 2859. https://doi.org/10.3390/su13052859

    Article  Google Scholar 

  • Liu, W., Yang, T., Sun, F., Wang, H., Feng, Y., & Du, M. (2021b). Observation-constrained projection of global flood magnitudes with anthropogenic warming. Water Resources Research, 57(3), e2020WR028830.

    Article  ADS  Google Scholar 

  • Malik, S., Pal, S. C., Arabameri, A., Chowdhuri, I., Saha, A., Chakrabortty, R., et al. (2021). GIS-based statistical model for the prediction of flood hazard susceptibility. Environment, Development and Sustainability, 23, 16713–16743.

    Article  Google Scholar 

  • Meraj, G., Yousuf, A. R., & Romshoo, S. A. (2013). Impacts of the Geo-environmental setting on the flood vulnerability at watershed scale in the Jhelum basin.” M Phil dissertation. University of Kashmir.

    Google Scholar 

  • Meraj, G., Farooq, M., Singh, S. K., Islam, M. N., & Kanga, S. (2022). Modeling the sediment retention and ecosystem provisioning services in the Kashmir valley, India, Western Himalayas. Modeling Earth Systems and Environment, 8(3), 3859–3884.

    Article  Google Scholar 

  • Meraj, G., Romshoo, S. A., Ayoub, S., & Altaf, S. (2018). Geoinformatics based approach for estimating the sediment yield of the mountainous watersheds in Kashmir Himalaya India. Geocarto International, 33(10), 1114–1138.

    Article  ADS  Google Scholar 

  • Meraj, G., Romshoo, S. A., Yousuf, A. R., Altaf, S., & Altaf, F. (2015). Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Natural Hazards, 77, 153–175.

    Article  Google Scholar 

  • Meraj, G., Singh, S. K., Kanga, S., & Islam, M. N. (2021). Modeling on comparison of ecosystem services concepts, tools, methods and their ecological-economic implications: A review. Modeling Earth Systems and Environment, 1–20.

  • Meyer, V., Haase, D., & Scheuer, S. (2009). Flood risk assessment in European river basins—concept, methods, and challenges exemplified at the Mulde River. Integrated Environmental Assessment and Management, 5(1), 17–26. https://doi.org/10.1897/ieam_2008-031.1

    Article  PubMed  Google Scholar 

  • Mitra, R., & Das, J. (2023). 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, 30(6), 16036–16067. https://doi.org/10.1007/s11356-022-23168-5

    Article  PubMed  Google Scholar 

  • Mitra, R., Saha, P., & Das, J. (2022). Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India. Geomatics, Natural Hazards and Risk, 13(1), 2183–2226. https://doi.org/10.1080/19475705.2022.2112094

    Article  Google Scholar 

  • Mitra, S., & Santra, A. (2020). Monitoring urban expansion and land use/land cover changes of Agartala City, Tripura Inndia. Indian Journal of Regional Science, 52(1), 123–136.

    Google Scholar 

  • Mitra, S., Roy, S., & Hore, S. (2023). Assessment and forecasting of the urban dynamics through lulc based mixed model: evidence from Agartala, India. GeoJournal, 88, 2399–2422. https://doi.org/10.1007/s10708-022-10730-4

    Article  Google Scholar 

  • Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghaz-ali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102.

    Article  Google Scholar 

  • Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5, 3–30. https://doi.org/10.1002/hyp.3360050103

    Article  ADS  Google Scholar 

  • NDMA (2022). A publication of the National Disaster Management Authority, Government of India. .

    Google Scholar 

  • Neog, R. (2022). Evaluation of temporal dynamics of land use and land surface temperature (LST) in Agartala city of India. Environment, Development and Sustainability, 24(3), 3419–3438. https://doi.org/10.1007/s10668-021-01572-0

    Article  Google Scholar 

  • NIDM. (2011). India Disaster Report 2011. National Institute of Disaster Management. https://nidm.gov.in/PDF/pubs/India%20Disaster%20Report%202011.pdf.

  • Notaro, V., De Marchis, M., Fontanazza, C. M., La Loggia, G., Puleo, V., & Freni, G. (2014). The effect of damage functions on urban flood damage appraisal. Procedia Engineering, 70, 1251–1260.

    Article  Google Scholar 

  • Olsen, J. R., Beling, P. A., & Lambert, J. H. (2000). Dynamic models for floodplain management. Journal of Water Resources Planning and Management, 126, 167–175.

    Article  Google Scholar 

  • Pal, S. C., Chowdhuri, I., Das, B., Chakrabortty, R., Roy, P., Saha, A., & Shit, M. (2022). Threats of climate change and land use patterns enhance the susceptibility of future floods in India. Journal of Environmental Management, 305, 114317.

    Article  PubMed  Google Scholar 

  • Parvin, F., Ali, S. A., Calka, B., Bielecka, E., Linh, N. T. T., & Pham, Q. B. (2022). Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms. Theoretical and Applied Climatology, 149(1-2), 639–659.

    Article  ADS  Google Scholar 

  • Pawe, C. K., & Saikia, A. (2023). Simulating urban land use change trajectories in Guwahati city, India. International Journal of Environmental Science and Technology, 20. https://doi.org/10.1007/s13762-023-05305-w

  • Pham, B. T., Jaafari, A., Van Phong, T., Yen, H. P. H., Tuyen, T. T., Van Luong, V., Nguyen, H. D., Van Le, H., & Foong, L. K. (2021). Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosciences Frontiers, 12(3), 101105. https://doi.org/10.1016/J.GSF.2020.11.003

    Article  Google Scholar 

  • Pham, Q. B., Chandra Pal, S., Saha, A., Chowdhuri, I., Albanai, J. A., Janizadeh, S., Ahmadi, K., Khedher, K. M., Anh, D. T., & Duan, W. (2022). Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region. Geocarto International, 37(25), 9047–9070.

    Article  ADS  Google Scholar 

  • Poudyal, C. P., Chang, C., Oh, H. J., & Lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environmental Earth Science, 61(5), 1049–1064. https://doi.org/10.1007/s12665-009-0426-5

    Article  ADS  Google Scholar 

  • Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Science, 60, 1037–1054.

    Article  ADS  Google Scholar 

  • Pradhan, B., Hagemann, U., Tehrany, M. S., & Prechtel, N. (2014). An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Computers & Geosciences, 63, 34–43.

    Article  ADS  Google Scholar 

  • Rafiei-Sardooi, E., Azareh, A., Choubin, B., Mosavi, A. H., & Clague, J. J. (2021). Evaluating urban flood risk using hybrid method of TOPSIS and machine learning. International Journal of Disaster Risk Reduction, 66, 102614.

    Article  Google Scholar 

  • Rafiq, F., Ahmed, S., Ahmad, S., & Khan, A. A. (2016). Urban floods in India. International Journal of Scientific & Engineering Research, 7(1), 721–734.

    Google Scholar 

  • Rahman, M., Ningsheng, C., Islam, M. M., Dewan, A., Iqbal, J., Washakh, R. M. A., & Shufeng, T. (2019). Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Systems and Environment, 3, 585–601. https://doi.org/10.1007/s41748-019-00123-y

    Article  Google Scholar 

  • Ramiaramanana, F. N., & Teller, J. (2021). Urbanization and floods in sub-saharan africa: spatiotemporal study and analysis of vulnerability factors—Case of Antananarivo agglomeration (Madagascar). Water, 13, 149. https://doi.org/10.3390/w13020149

    Article  Google Scholar 

  • Rani, A., Gupta, S. K., Singh, S. K., Meraj, G., Kumar, P., Kanga, S., Đurin, B., & Dogančić, D. (2023). Predicting future land use utilizing economic and land surface parameters with ANN and Markov chain models. Earth, 4(3), 728–751.

    Article  ADS  Google Scholar 

  • Rather, M. A., Meraj, G., Farooq, M., Shiekh, B. A., Kumar, P., Kanga, S., Singh, S. K., Sahu, N., & Tiwari, S. P. (2022). Identifying the potential dam sites to avert the risk of catastrophic floods in the Jhelum Basin, Kashmir, NW Himalaya India. Remote Sensing, 14(7), 1538.

    Article  ADS  Google Scholar 

  • Razafindrabe, B. H., Kada, R., Arima, M., & Inoue, S. (2014). Analyzing flood risk and related impacts to urban communities in central Vietnam. Mitigation and Adaptation Strategies for Global Change, 19, 177–198.

    Article  Google Scholar 

  • Ruidas, D., Saha, A., Islam, A. R. M. T., Costache, R., & Pal, S. C. (2022). Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river India. Environmental Science and Pollution Research, 1–16.

  • Saha, A., Pal, S. C., Arabameri, A., Blaschke, T., Panahi, S., Chowdhuri, I., Chakrabortty, R., Costache, R., & Arora, A. (2021). Flood susceptibility assessment using novel ensemble of hyper pipes and support vector regression algorithms. Water, 13(2), 241. https://doi.org/10.3390/w13020241

    Article  Google Scholar 

  • Saikh, N. I., & Mondal, P. (2023). Gis-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India. Natural Hazards Research.

  • Santra, A., Mitra, S., & Debbarma, D. (2018). Impact of urbanization on land use changes in Agartala City, India. Research Journal of Humanities and Social Sciences, 9(2), 407–414.

    Article  Google Scholar 

  • Sarhadi, A., Soltani, S., & Modarres, R. (2012). Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis. Journal of Hydrology, 458, 68–86.

    Article  ADS  Google Scholar 

  • Sarkar, D., & Mondal, P. (2020). Flood vulnerability mapping using frequency ratio (FR) model: A case study on Kulik river basin Indo-Bangladesh Barind region. Applied Water Science, 10(1), 1–13.

    Article  CAS  Google Scholar 

  • Scionti, F., Miguez, M. G., Barbaro, G., De Sousa, M. M., Foti, G., & Canale, C. (2018). Integrated methodology for urban flood risk mitigation in Cittanova, Italy. Journal of Water Resources Planning and Management, 144(10), 05018013.

    Article  Google Scholar 

  • Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S., Pham, B. T., Mansouripour, F., Geertsema, M., Clague, J. J., & Bui, D. T. (2021). Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosciences Frontiers, 12(3), 101100. https://doi.org/10.1016/j.gsf.2020.10.007

    Article  Google Scholar 

  • Shang, J., & Wilson, J. P. (2009). Watershed urbanization and changing flood behaviour across the Los Angeles metropolitan region. Natural Hazards, 48, 41–57.

    Article  Google Scholar 

  • Sharma, M., Upadhyay, R. K., Tripathi, G., Kishore, N., Shakya, A., Meraj, G., Kanga, S., et al. (2023). Assessing landslide susceptibility along India’s National Highway 58: A comprehensive approach integrating remote sensing, GIS, and logistic regression analysis. Conservation, 3(3), 444–459.

    Article  Google Scholar 

  • Sivapalan, M., Savenije, H. H., & Blöschl, G. (2012). Socio-hydrology: A new science of people and water. Hydrological Processes, 26(8), 1270–1276.

    Article  ADS  Google Scholar 

  • Station: Agartala (A) Climatological Table 1981–2010 (PDF). (2015). In Climatological normals 1981–2010. India Meteorological Department. pp. 5–6. Archived from the original (PDF) on 5 February 2020.

  • Sud, A., Kanga, R., Singh, S. K., Meraj, G., Kanga, S., Kumar, P., Ramanathan, A. L., & Bhardwaj, V. (2023). Simulating groundwater potential zones in mountainous Indian Himalayas—A case study of Himachal Pradesh. Hydrology, 10(3), 65.

    Article  Google Scholar 

  • Sundaram, S., Devaraj, S., & Yarrakula, K. (2021). Modeling, mapping and analysis of urban floods in India—A review on geospatial methodologies. Environmental Science and Pollution Research, 1–17.

  • Suriya, S., & Mudgal, B. V. (2012). Impact of urbanization on flooding: The Thirusoolam sub watershed–A case study. Journal of hydrology, 412, 210–219.

    Article  ADS  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping 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  ADS  Google Scholar 

  • Tomar, P., Singh, S. K., Kanga, S., Meraj, G., Kranjčić, N., Đurin, B., & Pattanaik, A. (2021). GIS-based urban flood risk assessment and management—A case study of Delhi National Capital Territory (NCT) India. Sustainability, 13(22), 12850.

    Article  Google Scholar 

  • Vemula, S., Raju, K. S., Veena, S. S., & Kumar, A. S. (2019). Urban floods in Hyderabad, India, under present and future rainfall scenarios: a case study. Natural hazards, 95, 637–655.

    Article  Google Scholar 

  • Wang, Y., Fang, Z., Hong, H., Costache, R., & Tang, X. (2021). Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management, 289, 112449. https://doi.org/10.1016/j.jenvman.2021.112449

    Article  PubMed  Google Scholar 

  • Yazdi, J., & Salehi Neyshabouri, S. A. A. (2012). A simulation-based optimization model for flood management on a watershed scale. Water Resources Management, 26, 4569–4586.

    Article  Google Scholar 

  • Zhang, Y. Y., Shao, Q. X., & Zhao, T. T. G. (2017). Comprehensive assessment of dam impacts on flow regimes with consideration of interannual variations. Journal of Hydrology, 552, 447–459.

Download references

Acknowledgements

The authors acknowledge the Survey of India (SOI) for providing the toposheet and the United States Geological Survey (USGS) for providing free satellite images. Moreover, we thank the local people for their valuable cooperation during the field verification. JD is grateful to the University Grants Commission, New Delhi, for awarding him as the Dr. D.S. Kothari Post-Doctoral Fellowship (UGC-DSKPDF). JD, DS, and AS acknowledge using computing facilities at the Prof. M.M. Das Advanced Study and Resource Cell at the Department of Geography, Gauhati University.

Author information

Authors and Affiliations

  1. Department of Geography, Gauhati University, Guwahati, Assam, 781014, India

    Jatan Debnath, Dhrubajyoti Sahariah & Anup Saikia

  2. Department of Geography & Disaster Management, Tripura University, Agartala, Tripura, India

    Jimmi Debbarma

  3. Department of Forestry & Biodiversity, Tripura University, Agartala, Tripura, India

    Amal Debnath

  4. Department of Ecosystem Studies, University of Tokyo, Bunkyo City, Tokyo, Japan

    Gowhar Meraj

  5. Centre for Environmental Assessment & Climate Change, GB Pant National Institute of Himalayan Environment (NIHE), Himachal Regional Centre (Himachal Pradesh), Kullu, India

    Kesar Chand

  6. Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur, India

    Suraj Kumar Singh

  7. Department of Geography , Central University of Punjab, Bathinda, India

    Shruti Kanga

  8. Institute for Global Environmental Strategies, Hayama, Japan

    Pankaj Kumar

Authors
  1. Jatan Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jimmi Debbarma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amal Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gowhar Meraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kesar Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suraj Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shruti Kanga
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pankaj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dhrubajyoti Sahariah
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Anup Saikia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jatan Debnath: conceptualization, methodology, software, data curation, and writing—original draft preparation. Jimmi Debbarma: conceptualization, data collection, preparation, writing, review, and editing. Amal Debnath: conceptualization, data collection for FIM preparation, writing, review, and editing. Gowhar Meraj: conceptualization, methodology, data curation, and writing—original draft preparation. Kesar Chand: methodology, writing, and review. Suraj Kumar Singh: methodology, writing, and review. Shruti Kanga: methodology and writing, and review. Pankaj Kumar: methodology, writing, and review. Dhrubajyoti Sahariah: writing and review. Anup Saikia: review and editing. All authors contributed to the finalization of the manuscript.

Corresponding author

Correspondence to Jatan Debnath.

Ethics declarations

Ethical approval

All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors and are aware that, with minor exceptions, no changes can be made to authorship once the paper is submitted.

Consent to participate

Agree to participate.

Consent to publish

Agree to publish.

Competing interests

The authors declare no competing interests.

Additional information

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

Debnath, J., Debbarma, J., Debnath, A. et al. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm. Environ Monit Assess 196, 110 (2024). https://doi.org/10.1007/s10661-023-12240-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-12240-3

Keywords

Search

Navigation