Coastal Sea Line Dynamics Using Machine Learning and LSTM Based Deep Learning Models
19 Pages Posted: 20 Dec 2024 Publication Status: Under Review
Abstract
Rising sea levels pose an imminent hazard to coastal communities that depend on the sea for their livelihood. Hence, it is the need of the hour to devise suitable computational techniques to forecast the rise in sea levels and the resulting change in shoreline dynamics to make informed decisions on future resettlement and disaster mitigation. The proposed initiative aims to predict the change in the coastline in the Krishna-Godavari delta. The study utilizes historical data on shoreline dynamics, incorporating environmental parameters such as tidal surge, u and v components of wind, distance weight, surge level, and mean sea level (in Ms. Pl). This data is then computed over a long-short-term memory (LS-TM) model to obtain the predicted surge values. This data is employed to predict forthcoming shoreline changes, subsequently visualized through the elevation map layer of Google Earth Engine to provide reliable yet foolproof visualization of submersion-prone regions. In conclusion, the stated paper aims to harness the power of deep learning to predict the future of coastal sealine.
Keywords: Tidal Gauge, RNN, LSTM, ARIMA, Google App Engine
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- Abstract Views: 31
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