A COMPARATIVE REVIEW OF DETECTION AND FORECASTING OF COASTAL EROSION PATTERNS
Keywords:
Keywords:Machine Learning(ML), Convolutional Neural Networks (CNN), Image Processing. Disaster Rescue, IoT (Internet of Things), Geographic Information Systems (GIS), Deep Learning (DL), Artificial Intelligence (AI).
Abstract
Coastal erosion, driven by natural processes and exacerbated by climate change and human activities, poses a significant threat to coastal ecosystems, infrastructure, and livelihoods. Accurate detection and forecasting of coastal erosion patterns are crucial for effective management and mitigation strategies. This paper presents a comparative review of methodologies and technologies employed in the detection and forecasting of coastal erosion. The study examines traditional approaches, such as field surveys and statistical modeling, alongside modern innovations, including machine learning (ML), Deep Learning (DL), Geographic Information Systems (GIS), and remote sensing technologies. The review highlights the transformative potential of Artificial Intelligence (AI)-enabled solutions in improving the accuracy and scalability of erosion monitoring systems. Key advances in AI models, such as Convolutional Neural Networks (CNNs) for image processing and Random Forest algorithms for predictive analytics, are analyzed for their contributions to understanding erosion dynamics. Furthermore, the integration of satellite imagery, drone-based surveys, and Internet of Things (IoT) devices has enhanced real-time monitoring and data acquisition capabilities, enabling more timely and effective interventions.This comparative analysis identifies the strengths, limitations, and applicability of various methods across different coastal environments. While AI and remote sensing technologies have advanced detection and forecasting capabilities, challenges such as data availability, computational complexity, and model interpretability persist. The paper concludes by outlining future research directions and technological innovations needed to develop robust, scalable, and adaptive systems for managing the growing risks of coastal erosion in a changing climate.
References
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[1] Y. F. Huang, S. Y. Ang, K. M. Lee, and T. S. Lee, ``Quality of water resources in Malaysia,'' Res. Practices Water Qual., T. S. Lee, Ed. Rijeka, Croatia: Books on Demand, 2015, pp. 65_94, doi: 10.5772/58969.
[2] D. M. Anderson, ``Approaches to monitoring, control and management of harmful algal Blooms (HABs),'' Ocean Coast Manag., vol. 52, no. 7, p. 342, Jul. 2009, doi: 10.1016/j.ocecoaman.2009.04.006.
[3] N. A. P. Rostam, N. H. Ahamed, H. Malim, and R. Abdullah, ``Development of a low-cost solar powered & real-time water quality monitoring system for Malaysia seawater aquaculture: Application & challenges,'' in Proc. 4th Int. Conf. Cloud Big Data Comput., Virtual, U.K., Aug. 2020, pp. 86_91, doi: 10.1145/3416921.3416928.
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[5] R. Ande, B. Adebisi, M. Hammoudeh, and J. Saleem, ``Internet of Things: Evolution and technologies from a security perspective,'' Sustain. Cities Soc., vol. 54, Mar. 2020, Art. no. 101728, doi: 10.1016/j.scs.2019.101728.
[6] S. Sharma, K. Hamal, N. Khadka, and B. B. Joshi, ``Dominant pattern of year-to-year variability of summer precipitation in nepal during
1987_2015,'' Theor. Appl. Climatol., vol. 142, nos. 3_4, pp. 1071_1084, Aug. 2020.
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[9] A. Wiese, J. Staneva, J. Schulz-Stellen_eth, A. Behrens, L. Fenoglio-Marc, and J.-R. Bidlot, ``Synergy of wind wave model
simulations and satellite observations during extreme events,'' Ocean Sci., vol. 14, no. 6, pp. 1503_1521, Dec. 2018.
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[11] T. M. Thanh, H. Tanaka, Y. Mitobe, N. T. Viet, and R. Almar, ``Seasonal variation of morphology and sediment movement on Nha Trang Coast, Vietnam,'' J. Coastal Res., vol. 81, no. 1, pp. 22_31, Sep. 2018.
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net/pdf/Protected_Planet_Report_2018.pdf
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[14] N. Pettorelli et al., ``Satellite remote sensing of ecosystem functions: Opportunities, challenges and way forward,'' Remote Sens. Ecology Conservation, vol. 4, no. 2, pp. 71_93, Aug. 2017.
[15] M. Chu and H. Zhang, ``Comparison experiment of sun glint correction method for nearshore high-resolution multispectral satellite images,'' Proc. SPIE, vol. 10850, Dec. 2018, Art. no. 108500R.
[16] J. Marcello, F. Eugenio, J. Martín, and F. Marqués, ``Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery,'' Remote Sens., vol. 10, no. 8, p. 1208, Aug. 2018.
[17] R. Dellink, E. Lanzi, and J. Chateau, “The sectoral and regional economic consequences of climate change to 2060,” Environ. Resour. Econ., vol. 72, no. 2, pp. 309–363, 2019.
[18] S. K. Sood and K. S. Rawat, “A scientometric analysis of ICTassisted disaster management,” Nat. Hazards, vol. 106, pp. 2863–2881, Jan. 2021.
[19] H. Shakhatreh et al., “Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,” IEEE Access, vol. 7, pp. 48572–48634, 2019.
[20] M. A. R. Estrada and A. Ndoma, “The uses of unmanned aerial vehicles—UAV’s- (or drones) in social logistic: Natural disasters response and humanitarian relief aid,” Procedia Comput. Sci., vol. 149, pp. 375–383, Mar. 2019.
[21] Y. E. Wang, G.-Y. Wei, and D. Brooks, “Benchmarking TPU, GPU, and CPU platforms for deep learning,” 2019. [Online]. Available: arXiv:1907.10701.
[22] J. Pretty, “Intensification for redesigned and sustainable agricultural systems,”Science, vol. 362, no. 23, 2018, Art. no. eaav0294.
[23] S. Devi, “Locust swarms in East Africa could be “a catastrophe” Lancet, vol. 395, 2020, Art. no.10224.
[24] J. C. Zhang et al., “Monitoring plant diseases and pests through remote sensing technology: A review,” Comput. Electron. Agriculture, no. 165, 2019, Art. no. 104943.
[25] H. Al-Saddik, A. Laybros, B. Billiot, and F. Cointault, “Using image texture and spectral reflectance analysis to detect yellowness and ESCA in grapevines at leaf-level,” Remote Sens., vol. 10, no. 4, 2018, Art. no. 618.
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[28] M. Zaharia, R. S. Xin, P. Wendell, T. Das, and I. Stoica, “Apache Spark: A unified engine for big data processing,” Commun. ACM, vol. 59, no. 11, pp. 56–65, 2016.
[29] F. Xu et al., “Big data driven mobile traffic understanding and forecasting: A time series approach,” IEEE Trans. Serv. Comput., vol. 9, no. 5,
pp. 796–805, Sep./Oct. 2017.
[30] B. Jiang, J. Yang, Z. Lv, and H. Song, “Wearable vision assistance system based on binocular sensors for visually impaired users,” IEEE Internet Things J., vol. 6, no. 2, pp. 1375–1383, Apr. 2019.
[31] B. Jiang, J. Yang, H. Xu, H. Song, and G. Zheng, “Multimedia data throughput maximization in internet-of-things system based on optimization of cache-enabled UAV,” IEEE Internet Things J., vol. 6, no. 2, pp. 3525–3532, Apr. 2019.
[32] T. Daniele et al., “Mapping river bathymetries: Evaluating topobathymetric
LiDAR survey,” Earth Surf. Processes Landforms, vol. 44, no. 2,pp. 403–678, 2018.
[33] R. Schwarz, G. Mandlburger, M. Pfennigbauer, and N. Pfeifer, “Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters,” ISPRS J. Photogram. Remote Sens., vol. 150, pp. 1–10, 2019.
[34] B.Chen,Y.Yang, D. Xu, and E. Huang, “A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth,” ISPRS J. Photogram. Remote Sens., vol. 151, pp. 1–13 2019. [7] J.M. Kerr, and S. Purkis, “An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data,” Remote Sens. Environ., vol. 210, pp. 307–324, 2018.
[35] C. Danilo, and F. Melgani, “High-coverage satellite-based coastal bathymetry through a fusion of physical and learning methods,” Remote Sens., vol. 11, no. 4, 2019.
[36] S. Gonzalo, C. Daniel, L. Pau, O. Alejandro, and R. Francesca, “UBathy: A new approach for bathymetric inversion from video imagery,” Remote Sens., vol. 11, no. 23, 2019.
[37] R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, and R. Shibasaki, ‘‘DeepCrowd: A deep model for largescale citywide crowd density and flow prediction,’’ IEEE Trans. Knowl. Data Eng., vol. 35, no. 1, pp. 276–290, Jan. 2023, doi:
10.1109/TKDE.2021.3077056.
[38] W. Jin, J. Yang, and Y. Fang, ‘‘Application methodology of big data for emergency management,’’ in Proc. IEEE 11th Int. Conf.Softw. Eng. Service Sci. (ICSESS), Oct. 2020, pp. 326–330, doi: 10.1109/ICSESS49938.2020.9237653.
[39] N. Al-Nabhan, S. Alenazi, S. Alquwaifili, S. Alzamzami, L. Altwayan, N. Alaloula, R. Alowaini, and A. B. M. A. A. Islam, ‘‘An intelligent IoT approach for analyzing and managing crowds,’’ IEEE Access, vol. 9, pp. 104874–104886, 2021, doi: 10.1109/ACCESS.2021.3099531.
[40] S. Mishra, M. K. Jena, and A. Kumar Tripathy, ‘‘Towards the development of disaster management tailored machine learning systems,’’ in Proc. IEEE India Council Int. Subsections Conf. (INDISCON), Jul. 2022, pp. 1–6, doi: 10.1109/INDISCON54605.2022.9862877.
[41] X. Li, Q. Yu, B. Alzahrani, A. Barnawi, A. Alhindi, D. Alghazzawi, and Y. Miao, ‘‘Data fusion for intelligent crowd monitoring and management systems: A survey,’’ IEEE Access, vol. 9, pp. 47069–47083, 2021, doi: 10.1109/ACCESS.2021.3060631.
[42] X. Wang et al., “Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images,” Remote Sens., vol. 11, no. 16, p. 1927, Aug. 2019.
[43] N. B. Toosi, A. R. Soffianian, S. Fakheran, S. Pourmanafi, C. Ginzler, and L. T. Waser, “Land cover classification in mangrove ecosystems
based on VHR satellite data and machine learning—An upscaling approach,” Remote Sens., vol. 12, no. 17, p. 2684, Aug. 2020.
[44] M. L. Zoffoli et al., “Sentinel-2 remote sensing of Zostera Nolteidominated intertidal seagrass meadows,” Remote Sens. Environ., vol. 251, Dec. 2020, Art. no. 112020.
[45] X. Wang et al., “Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth engine,” Remote Sens. Environ., vol. 238, Mar. 2020, Art. no. 110987.
[46] L. Wang, M. Jia, D. Yin, and J. Tian, “A review of remote sensing for mangrove forests: 1956–2018,” Remote Sens. Environ., vol. 231, Sep. 2019, Art. no. 111223.
[47] Sathiyabhama, B., Kumar, S.U., Jayanthi, J., ... Yuvarajan, V., Gopikrishna, K. “A Novel Feature Selection Framework Based On Grey Wolf Optimizer For Mammogram Image Analysis,” Neural Computing and Applications., 2021, 33(21), pp. 14583–14602.
Published
2024-12-04
How to Cite
J.B. Vaishnavi, & Dr.R. Sivakami. (2024). A COMPARATIVE REVIEW OF DETECTION AND FORECASTING OF COASTAL EROSION PATTERNS. Revista Electronica De Veterinaria, 25(2), 543 -553. https://doi.org/10.69980/redvet.v25i2.1398
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