International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 06 | June 2024
p-ISSN: 2395-0072
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Prediction of Coastal Erosion Rates Using AI Technology Vaishnavi Chandran1, Dr. Rani V 2 1M. Tech Student, Civil Engineering Department, Marian Engineering College, Thiruvananthapuram, Kerala, India 2Associate Professor, Civil Engineering Department, Marian Engineering College, Thiruvananthapuram,
Kerala, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Coastal Erosion is a major concern in countries
consideration when analysing shoreline change, aside from the typical seasonal dynamics of shoreline.
having coastal zone, and India has very longest coastal zone. In coastal erosion the slope stability is affected rather than the soil particles. About 370 km of Kerala coast is subjected to coastal erosion of various magnitudes, which can be due to several factors like early onslaught of monsoon and subsequent high and steep waves. 23% of Trivandrum's coastal line was affected in the year 2021. Considering some recent researches, it was seen that the coastal areas of Thiruvananthapuram district such as Menamkulam, Puthenthope, Veli, Adimalathura, Valiathura etc. are prone to coastal erosion. The aim of this study is to analyse these identified vulnerable coastal regions and the impacts of coastal erosion caused due to the varying wave action and the beach slopes in these sites on the basis of INCOIS data. Based on these ideas and use of emerging AI Technology, a code is developed which can be used to predict the total erosion or accretion that can occur on these sites.
2. LITERATURE REVIEW J. Shaji (2020) studied the Thiruvananthapuram district of Kerala, along the southwest coast of India, which is a densely populated coastline and is sensitive to sea surge and severe coastal erosion. This coastal zone is sensitive, as evidenced by the fact that it was flooded in multiple places by the Indian Ocean Tsunami of December 2004. Sensitivity of the coast if considered in conjunction with other social factors may be an input into broader assessments of the overall vulnerability of coasts and their communities [1]. Sheela Nair et al.(2018) made a recent study that uses data from satellite pictures and the 1968 SOI topographic chart to investigate long-term coastline changes along the southwest coast of India from 1968 to 2014. The USGS DSAS programme was utilised to calculate the rate of changes. The study discovered that man-made activities including building dams, hard structure development, and mining sand from beaches and rivers had drastically altered the whole width of the Kerala coast. The paper makes the argument that since shoreline features are constantly changing as a result of both natural and human activity, forecasting future trends may not be feasible. [2]
Key Words: Soil erosion, Wave action, beach slopes, AI codes
1. INTRODUCTION In order to address the growing threat to life and property in the coastal zone, precise information on coastal erosion based on historical and recent shoreline changes is crucial. These changes could be long term, short term, seasonal or episodic and result in coastal erosion or accretion. Waves, currents, tides and winds, as well as anthropogenic activities cause coastal erosion. One of the most reliable signs of coastal erosion is coastline retreat. A variety of techniques have been put forth to quantify coastal retreat. The baseline approach, area-based approach, dynamic segmentation approach, buffering, and nonlinear least squares estimation approach are these. Numerous scholars have examined the characteristics of coastal erosion in the area surrounding Thiruvananthapuram, which lies on India's southwest coast. Prior research has established a baseline understanding of shoreline change and related erosion and accretion along this coast through the use of Survey of India toposheets, satellite imagery, and aerial images. Thiruvananthapuram is the zone where erosion occurs most frequently along the Keralan coast, according to a recent research by the National Centre for Sustainable Coastal Management. The unique morphologies along this high energy coast were taken into
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Kim and Lee (2022) in their studies highlight the advantages of machine learning (ML) over traditional regression techniques in coastal and ocean engineering. Many research on forecasting coastal engineering characteristics like waves, wave breaking, hydraulic properties, and beach profile changes have been made. The study focuses on regression analysis using continuous variables in supervised machine learning models, excluding categorical variable classification. It provides a comprehensive review of technological advancements and application examples of ML models in coastal engineering [3]. Goldstein et al. (2019) made studies that explores the use of machine learning (ML) in coastal morphodynamics and sediment transport research, highlighting the shift from traditional empirical methods to data-driven strategies. It emphasizes the importance of computational methodology, data amount, nonlinearity, and high dimensionality. The study identifies various ML applications in sediment
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