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Mapping shoreline change using machine learning: a case study from the eastern Indian coast

  • Research Article - Hydrology
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Abstract

The continuous shift of shoreline boundaries due to natural or anthropogenic events has created the necessity to monitor the shoreline boundaries regularly. This study investigates the perspective of implementing artificial intelligence techniques to model and predict the realignment in shoreline along the eastern Indian coast of Orissa (now called Odisha). The modeling consists of analyzing the satellite images and corresponding reanalysis data of the coastline. The satellite images (Landsat imagery) of the Orissa coastline were analyzed using edge detection filters, mainly Sobel and Canny. Sobel and canny filters use edge detection techniques to extract essential information from satellite images. Edge detection reduces the volume of data and filters out worthless information while securing significant structural features of satellite images. The image differencing technique is used to determine the shoreline shift from GIS images (Landsat imagery). The shoreline shift dataset obtained from the GIS image is used together with the metrological dataset extracted from Modern-Era Retrospective analysis for Research and Applications, Version 2, and tide and wave parameter obtained from the European Centre for Medium-Range Weather Forecast for the period 1985–2015, as input parameter in machine learning (ML) algorithms to predict the shoreline shift. Artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) algorithm are used as a ML model in the present study. The ML model contains weights that are multiplied with relevant inputs/features to obtain a better prediction. The analysis shows wind speed and wave height are the most prominent features in shoreline shift prediction. The model’s performance was compared, and the observed result suggests that the ANN model outperforms the KNN and SVM model with an accuracy of 86.2%.

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References

  • Acharjya PP, Das R, Ghoshal D (2012) Study and comparison of different edge detectors for image segmentation. Glob J Comput Sci Technol 12:29–32

    Google Scholar 

  • Afzal MS, Bihs H, Kumar L (2020) Computational fluid dynamics modeling of abutment scour under steady current using the level set method. Int J Sediment Res 35:355–364

    Google Scholar 

  • Ahangarha M, Seydi ST, Shahhoseini R (2019) Hyperspectral change detection in wetland and water-body areas based on machine learning. In: International archives of the photogrammetry, remote sensing & spatial information sciences, geospatial conference 2019—joint conferences of SMPR and GI research, vol XLII-4/W18, pp 19–24

  • Ahmadian AS, Simons RR (2018) Estimation of nearshore wave transmission for submerged breakwaters using a data-driven predictive model. Neural Comput Appl 29(10):705–719

    Google Scholar 

  • Alesheikh AA, Ghorbanali A, Nouri N (2007) Coastline change detection using remote sensing. Int J Environ Sci Technol 4(1):61–66

    Google Scholar 

  • Alexakis DD, Agapiou A, Hadjimitsis DG, Retalis A (2012) Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis. Acta Geophys 60(3):959–984

    Google Scholar 

  • Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    Google Scholar 

  • Arce-Medina E, Paz-Paredes JI (2009) Artificial neural network modeling techniques applied to the hydrodesulfurization process. Math Comput Model 49(1–2):207–214

    Google Scholar 

  • Bagheri M, Ibrahim ZZ, Mansor SB, Manaf LA, Badarulzaman N, Vaghefi N (2019) Shoreline change analysis and erosion prediction using historical data of Kuala Terengganu, Malaysia. Environ Earth Sci 78(15):477

    Google Scholar 

  • Barman NK, Chatterjee S, Khan A et al (2014) Trends of shoreline position: an approach to future prediction for Balasore shoreline, Odisha, India. Open J Mar Sci 5(01):13

    Google Scholar 

  • Bazile R, Boucher MA, Perreault L, Leconte R (2017) Verification of ECMWF system 4 for seasonal hydrological forecasting in a northern climate. Hydrol Earth Syst Sci 21(11):5747

    Google Scholar 

  • Bosilovich MG, Chen J, Robertson FR, Adler RF (2008) Evaluation of global precipitation in reanalyses. J Appl Meteorol Climatol 47(9):2279–2299

    Google Scholar 

  • Bosilovich MG, Robertson FR, Takacs L, Molod A, Mocko D (2017) Atmospheric water balance and variability in the MERRA-2 reanalysis. J Clim 30(4):1177–1196

    Google Scholar 

  • Bouguerra H, Tachi SE, Derdous O, Bouanani A, Khanchoul K (2019) Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms. Acta Geophys 67(6):1649–1660

    Google Scholar 

  • Bruun P (1962) Sea-level rise as a cause of shore erosion. J Waterw Harb Div 88(1):117–132

    Google Scholar 

  • Canny JF (1986) A theory of edge detection. IEEE Trans Pattern Anal Mach Intell 8:147–163

    Google Scholar 

  • Chalabi A, Mohd-Lokman H, Mohd-Suffian I, Karamali K, Karthigeyan V, Masita M (2006) Monitoring shoreline change using ikonos image and aerial photographs: a case study of kuala terengganu area, Malaysia. In: ISPRS Commission VII mid-term symposium “Remote sensing: from pixels to processes”, Enschede, The Netherlands, pp 8–11

  • Chudzian P (2011) Radial basis function kernel optimization for pattern classification. In: Burduk R, Kurzyński M, Woźniak M, Żołnierek A (eds) Computer recognition systems, vol 4. Springer, Berlin, pp 99–108

    Google Scholar 

  • Coltori M (1997) Human impact in the holocene fluvial and coastal evolution of the Marche region, central Italy. Catena 30(4):311–335

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  • Dada OA, Agbaje AO, Adesina RB, Asiwaju-Bello YA (2019) Effect of coastal land use change on coastline dynamics along the Nigerian Transgressive Mahin mud coast. Ocean Coast Manag 168:251–264

    Google Scholar 

  • De Jong SM, Van der Meer FD (2007) Remote sensing image analysis: including the spatial domain, vol 5. Springer, Berlin

    Google Scholar 

  • de Rosnay P, Munoz-Sabater J, Albergel C, Isaksen L, English S, Drusch M, Wigneron JP (2020) SMOS brightness temperature forward modelling and long term monitoring at ECMWF. Remote Sens Environ 237(111):424

    Google Scholar 

  • Dee DP, Uppala SM, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer DP et al (2011) The era-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597

    Google Scholar 

  • Dellepiane S, De Laurentiis R, Giordano F (2004) Coastline extraction from sar images and a method for the evaluation of the coastline precision. Pattern Recogn Lett 25(13):1461–1470

    Google Scholar 

  • Di Silvio G, Nones M (2014) Morphodynamic reaction of a schematic river to sediment input changes: analytical approaches. Geomorphology 215:74–82

    Google Scholar 

  • Dickens K, Armstrong A (2019) Application of machine learning in satellite derived bathymetry and coastline detection. SMU Data Sci Rev 2(1):1–25

    Google Scholar 

  • Dolan R, Fenster MS, Holme SJ (1991) Temporal analysis of shoreline recession and accretion. J Coast Res 7:723–744

    Google Scholar 

  • Dutta D, Mandal A, Afzal MS (2020) Discharge performance of plan view of multi-cycle w-form and circular arc labyrinth weir using machine learning. Flow Meas Instrum 73:101740

    Google Scholar 

  • ECMWF (2018) European centre for medium-range weather forecasts. https://www.ecmwf.int/en/research/modelling-and-prediction/marine

  • Elko N, Sallenger A, Guy K, Stockdon H, Morgan K (2002) Barrier island elevations relevant to potential storm impacts: 1. Techniques. US Geological Survey Open File Report, pp 02–287

  • Esteves LS, Williams JJ, Dillenburg SR (2006) Seasonal and interannual influences on the patterns of shoreline changes in Rio Grande do Sul, southern Brazil. J Coast Res 22:1076–1093

    Google Scholar 

  • Fadel S, Ghoniemy S, Abdallah M, Sorra HA, Ashour A, Ansary A (2016) Investigating the effect of different kernel functions on the performance of SVM for recognizing Arabic characters. Int J Adv Comput Sci Appl 7(1):446–450

    Google Scholar 

  • Garg A, Huang H, Kushvaha V, Madhushri P, Kamchoom V, Wani I, Koshy N, Zhu HH (2019) Mechanism of biochar soil pore–gas–water interaction: gas properties of biochar-amended sandy soil at different degrees of compaction using knn modeling. Acta Geophys 68:207–217

    Google Scholar 

  • Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. arXiv:150806576

  • Gazi AH, Afzal MS (2020) A new mathematical model to calculate the equilibrium scour depth around a pier. Acta Geophys 68(1):181–187

    Google Scholar 

  • Gazi AH, Afzal MS, Dey S (2019) Scour around piers under waves: current status of research and its future prospect. Water 11(11):2212

    Google Scholar 

  • Gelaro R, McCarty W, Molod A, Suarez M, Takacs L, Todling R (2014) The NASA modern era reanalysis for research and applications, Version-2 (MERRA-2). AGUFM 2014:NG32A–01

  • Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, Randles CA, Darmenov A, Bosilovich MG, Reichle R et al (2017) The modern-era retrospective analysis for research and applications, version 2 (merra-2). J Clim 30(14):5419–5454

    Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology. i: preliminary concepts. J Hydrol Eng 5(2):115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115)

    Article  Google Scholar 

  • Govindaraju RS (2000) Artificial neural networks in hydrology. ii: hydrologic applications. J Hydrol Eng 5(2):124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)

    Article  Google Scholar 

  • Green B (2002) Canny edge detection tutorial. Retrieved 6 Mar 2005

  • Gregory K (2004) River channel management. Hodder Education, London

    Google Scholar 

  • Guerrero M, Latosinski F, Nones M, Szupiany RN, Re M, Gaeta MG (2015) A sediment fluxes investigation for the 2-d modelling of large river morphodynamics. Adv Water Resour 81:186–198

    Google Scholar 

  • Gunawardena Y, Ilic S, Pinkerton H, Romanowicz R (2009) Nonlinear transfer function modelling of beach morphology at Duck, North Carolina. Coast Eng 56(1):46–58

    Google Scholar 

  • Gunn SR et al (1998) Support vector machines for classification and regression. ISIS Tech Rep 14(1):5–16

    Google Scholar 

  • Halpern BS, McLeod KL, Rosenberg AA, Crowder LB (2008) Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean Coast Manag 51(3):203–211

    Google Scholar 

  • Harley MD, Kinsela MA, Sánchez-García E, Vos K (2019) Shoreline change mapping using crowd-sourced smartphone images. Coast Eng 150:175–189

    Google Scholar 

  • Hashemi M, Ghadampour Z, Neill S (2010) Using an artificial neural network to model seasonal changes in beach profiles. Ocean Eng 37(14–15):1345–1356

    Google Scholar 

  • Houser C, Hapke C, Hamilton S (2008) Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. Geomorphology 100(3–4):223–240

    Google Scholar 

  • Howarth PJ, Wickware GM (1981) Procedures for change detection using landsat digital data. Int J Remote Sens 2(3):277–291

    Google Scholar 

  • Hsu HH, Hoskins BJ (1989) Tidal fluctuations as seen in ECMWF data. Q J R Meteorol Soc 115(486):247–264

    Google Scholar 

  • Hsu CW, Chang CC, Lin CJ et al (2003) A practical guide to support vector classification. Department of Computer Science National Taiwan University

  • Hu LY, Huang MW, Ke SW, Tsai CF (2016) The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5(1):1304

    Google Scholar 

  • Jan J, Hung SL, Chi S, Chern J (2002) Neural network forecast model in deep excavation. J Comput Civ Eng 16(1):59–65

    Google Scholar 

  • Jangir B, Satyanarayana A, Swati S, Jayaram C, Chowdary V, Dadhwal V (2016) Delineation of spatio-temporal changes of shoreline and geomorphological features of Odisha coast of India using remote sensing and gis techniques. Nat Hazards 82(3):1437–1455

    Google Scholar 

  • Kennedy AD, Dong X, Xi B, Xie S, Zhang Y, Chen J (2011) A comparison of MERRA and NARR reanalyses with the DOE ARM SGP data. J Clim 24(17):4541–4557

    Google Scholar 

  • Kesikoğlu MH, Çiçekli SY, Kaynak T (2020) The identification of coastline changes from landsat 8 satellite data using artificial using artificial neural networks and K-nearest neighbor. Turk J Eng 4(1):47–56

    Google Scholar 

  • Khaledian M, Isazadeh M, Biazar S, Pham Q (2020) Simulating Caspian sea surface water level by artificial neural network and support vector machine models. Acta Geophys 68:553–563

    Google Scholar 

  • Kim IH, Lee HS, Song DS (2013) Time series analysis of shoreline changes in Gonghyunjin and Songjiho Beaches, South Korea using aerial photographs and remotely sensed imagery. J Coast Res 65:1415–1420

    Google Scholar 

  • Kumar TS, Mahendra R, Nayak S, Radhakrishnan K, Sahu K (2010) Coastal vulnerability assessment for Orissa State, east coast of India. J Coast Res 26:523–534

    Google Scholar 

  • Larson M, Capobianco M, Hanson H (2000) Relationship between beach profiles and waves at Duck, North Carolina, determined by canonical correlation analysis. Mar Geol 163(1–4):275–288

    Google Scholar 

  • Lee YK, Eom J, Do JD, Kim BJ, Ryu JH (2019) Shoreline movement monitoring and geomorphologic changes of beaches using Lidar and UAVs Images on the Coast of the East Sea, Korea. J Coast Res 90(sp1):409–414

    Google Scholar 

  • Li R, Liu JK, Felus Y (2001) Spatial modeling and analysis for shoreline change detection and coastal erosion monitoring. Mar Geod 24(1):1–12

    Google Scholar 

  • Markose VJ, Rajan B, Kankara R, Selvan SC, Dhanalakshmi S (2016) Quantitative analysis of temporal variations on shoreline change pattern along Ganjam district, Odisha, East Coast of India. Environ Earth Sci 75(10):929

    Google Scholar 

  • MERRA-2 (2017) Modern era retrospective-analysis for research and applications. https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/

  • Mishra M, Chand P, Pattnaik N, Kattel DB, Panda G, Mohanti M, Baruah UD, Chandniha SK, Achary S, Mohanty T (2019) Response of long-to short-term changes of the Puri coastline of Odisha (India) to natural and anthropogenic factors: a remote sensing and statistical assessment. Environ Earth Sci 78(11):338

    Google Scholar 

  • Monalisha M, Panda G (2018) Coastal erosion and shoreline change in Ganjam coast along East Coast of India. J Earth Sci Clim Change 9:467

    Google Scholar 

  • Montaño J, Coco G, Antolínez JA, Beuzen T, Bryan KR, Cagigal L, Castelle B, Davidson MA, Goldstein EB, Ibaceta R et al (2020) Blind testing of shoreline evolution models. Sci Rep 10(1):1–10

    Google Scholar 

  • Morton R (1996) Geoindicators of coastal wetlands and shorelines. Geoindicators: assessment rapid environmental changes in earth systems. AA Balkema, Rotterdam, pp 207–230

    Google Scholar 

  • Mukhopadhyay A, Mukherjee S, Mukherjee S, Ghosh S, Hazra S, Mitra D (2012) Automatic shoreline detection and future prediction: a case study on Puri Coast, Bay of Bengal, India. Eur J Remote Sens 45(1):201–213

    Google Scholar 

  • Murthy VS, Gupta S, Mohanta D (2009) Distribution system insulator monitoring using video surveillance and support vector machines for complex background images. Int J Power Energy Convers 1(1):49–72

    Google Scholar 

  • Nandi S, Ghosh M, Kundu A, Dutta D, Baksi M (2016) Shoreline shifting and its prediction using remote sensing and gis techniques: a case study of Sagar Island, West Bengal (India). J Coast Conserv 20(1):61–80

    Google Scholar 

  • Nowakowski A (2015) Remote sensing data binary classification using boosting with simple classifiers. Acta Geophys 63(5):1447–1462

    Google Scholar 

  • Peponi A, Morgado P, Trindade J (2019) Combining artificial neural networks and gis fundamentals for coastal erosion prediction modeling. Sustainability 11(4):975

    Google Scholar 

  • Pescaroli G, Nones M, Galbusera L, Alexander D (2018) Understanding and mitigating cascading crises in the global interconnected system. Int J Disaster Risk Reduction 30:159–163

    Google Scholar 

  • Piasecki A, Jurasz J, Adamowski JF (2018) Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method. Acta Geophys 66(5):1093–1107

    Google Scholar 

  • Pierini JO, Lovallo M, Telesca L, Gómez EA (2013) Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina). Acta Geophys 61(6):1522–1537

    Google Scholar 

  • Puskarczyk E (2019) Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations. Acta Geophys 67(6):1991–2003

    Google Scholar 

  • Rajawat A, Chauhan H, Ratheesh R, Rode S, Bhanderi R, Mahapatra M, Kumar M, Yadav R, Abraham S, Singh S et al (2015) Assessment of coastal erosion along the Indian Coast on 1: 25,000 scale using satellite data of 1989–1991 and 2004–2006 time frames. Curr Sci 109:347–353

    Google Scholar 

  • Ramesh R, Purvaja R, Senthil Vel A (2011) National assessment of shoreline change: Odisha coast. NCSCM/ MoEF Report 2011-01, 57 p., available at http://www.ncscm.org/reports.php

  • Ramesh R, R P, Vel S (2017) A shoreline change assessment for Odisha Coast; National Centre for Sustainable Coastal Management (NCSCM). Govt. of Odisha Report. National Centre for Sustainable Coastal Management (NCSCM). Accessed on 11 Nov 2017

  • Reichle RH, Koster RD, De Lannoy GJ, Forman BA, Liu Q, Mahanama SP, Touré A (2011) Assessment and enhancement of merra land surface hydrology estimates. J Clim 24(24):6322–6338

    Google Scholar 

  • Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK et al (2011) Merra: Nasa’s modern-era retrospective analysis for research and applications. J Clim 24(14):3624–3648

    Google Scholar 

  • Ronco P, Fasolato G, Nones M, Di Silvio G (2010) Morphological effects of damming on lower Zambezi river. Geomorphology 115(1–2):43–55

    Google Scholar 

  • Ryan T, Sementilli P, Yuen P, Hunt B (1991) Extraction of shoreline features by neural nets and image processing. Photogramm Eng Remote Sens 57(7):947–955

    Google Scholar 

  • Saluja S, Singh AK, Agrawal S (2013) A study of edge-detection methods. Int J Adv Res Comput Commun Eng 2(1):994–999

    Google Scholar 

  • Satapathy SC, Udgata SK, Biswal BN (2012) Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA), vol 199. Springer, Berlin

    Google Scholar 

  • Schalkoff RJ (1997) Artificial neural networks, vol 1. McGraw-Hill, New York

    Google Scholar 

  • Shen S, Ostrenga D, Vollmer B, Li A, Meyer D (2019) MERRA-2 data and analytic services at NASA GES DISC for climate extremes study. In: 16th AOGS-Annual meeting of asia oceania geosciences society, July 28, 2019–August 02, 2019, Singapore

  • Shen S, Ostrenga DM, Bosilovich MG, Li AW, Meyer DJ (2020) Near 40 years MERRA-2 data at NASA GES DISC-opportunity and challenge to support extremes study. In: 100th AMS Annual Meeting, January 12, 2020–January 16, 2020, Boston, United States

  • Shrivakshan G, Chandrasekar C (2012) A comparison of various edge detection techniques used in image processing. Int J Comput Sci Issues: IJCSI 9(5):269

    Google Scholar 

  • Simeoni U, Corbau C (2009) A review of the delta po evolution (Italy) related to climatic changes and human impacts. Geomorphology 107(1–2):64–71

    Google Scholar 

  • Small C, Nicholls RJ (2003) A global analysis of human settlement in coastal zones. J Coast Res 19:584–599

    Google Scholar 

  • Sobel I, Feldman G (1968) A 3 \(\times\) 3 isotropic gradient operator for image processing. A talk at the Stanford artificial project, pp 271–272

  • Stockdon HF, Doran KS, Sallenger AH Jr (2009) Extraction of lidar-based dune-crest elevations for use in examining the vulnerability of beaches to inundation during hurricanes. J Coast Res 53:59–65

    Google Scholar 

  • Suanez S, Cariolet JM, Cancouët R, Ardhuin F, Delacourt C (2012) Dune recovery after storm erosion on a high-energy beach: Vougot Beach, Brittany (France). Geomorphology 139:16–33

    Google Scholar 

  • The Indian Tide Tables-Part 1,1995: Indian and Selected Foreign Ports (1994) Surveyor general of India, printed by survey of India, Dehradun

  • Tsekouras GE, Trygonis V, Maniatopoulos A, Rigos A, Chatzipavlis A, Tsimikas J, Mitianoudis N, Velegrakis AF (2018) A hermite neural network incorporating artificial bee colony optimization to model shoreline realignment at a reef-fronted beach. Neurocomputing 280:32–45

    Google Scholar 

  • USGS (2017) United states geological survey. https://earthexplorer.usgs.gov

  • Valiela I (2004) Global coastal change. Blackwell, Oxford

    Google Scholar 

  • Valipour M, Tian D (2018) Comparing soil moisture dynamics in climate reanalyses, land surface models, and remote sensing retrievals over the continental united states. In: AGU Fall Meeting Abstracts

  • Valipour M, Banihabib M, Behbahani S (2012) Monthly inflow forecasting using autoregressive artificial neural network. J Appl Sci 12(20):2139–2147

    Google Scholar 

  • Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the arma, arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of dez dam reservoir. J Hydrol 476:433–441

    Google Scholar 

  • Vapnik V (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780

    Google Scholar 

  • Vapnik VN, Chervone AY (1965) On a class of pattern-recognition learning algorithms. Autom Remote Control 25(6):838

    Google Scholar 

  • Varrani A, Nones M, Gupana R (2019) Long-term modelling of fluvial systems at the watershed scale: examples from three case studies. J Hydrol 574:1042–1052

    Google Scholar 

  • Vijayarani S, Vinupriya M (2013) Performance analysis of Canny and Sobel edge detection algorithms in image mining. Int J Innov Res Comput Commun Eng 1(8):1760–1767

    Google Scholar 

  • Vincent OR, Folorunso O et al (2009) A descriptive algorithm for sobel image edge detection. In: Proceedings of informing science & IT education conference (InSITE), vol 40. Informing Science Institute California, pp 97–107

  • Wang J, Li B, Gao Z, Wang J (2019) Comparison of ECMWF significant wave height forecasts in the China sea with buoy data. Weather Forecast 34(6):1693–1704

    Google Scholar 

  • White K, El Asmar HM (1999) Monitoring changing position of coastlines using Thematic Mapper imagery, an example from the Nile Delta. Geomorphology 29(1–2):93–105

    Google Scholar 

  • Zhang X, Wang Z (2010) Coastline extraction from remote sensing image based on improved minimum filter. In: 2010 second IITA international conference on geoscience and remote sensing, vol 2. IEEE, pp 44–47

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Acknowledgements

We thank Mr. Sobhit and Mr. Satish Yadav (B. Tech students of IIT Kharagpur) for the assistance in writing code. This work was carried out as a part of the project titled “Predictive Tool for Arctic Coastal Hydrodynamics and Sediment Transport” funded by the National Centre for Polar and Ocean Research (NCPOR). Authors also acknowledge support by SRIC, IIT Kharagpur, under the ISIRD project titled ”3D CFD Modeling of the Hydrodynamics and Local Scour Around Offshore Structures Under Combined Action of Current and Waves.”

Funding

Funding was provided by Sponsored Research and Industrial Consultancy (Grant No. IIT/SRIC/CE/MOS/2017-18/200) and Ministry of Earth Sciences (Grant No. NCPOR/2019/PACER-POP/OS-02).

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  1. Department of Civil Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

    Lalit Kumar & Mohammad Saud Afzal

  2. Birlasoft Limited, Tower 3 Assotech Business Cresterra Plot no 22, Noida-Greater Noida Expy, Sector 135, Noida, Uttar Pradesh, India

    Mohammad Mashhood Afzal

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Kumar, L., Afzal, M.S. & Afzal, M.M. Mapping shoreline change using machine learning: a case study from the eastern Indian coast. Acta Geophys. 68, 1127–1143 (2020). https://doi.org/10.1007/s11600-020-00454-9

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