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Sea-level rise in response to climate change and global warming severely impacts coastal cities through increased soil erosion and other hazards. Therefore, simulating threats in coastal locations is critical for coastal city management and planning. The Nonlinear Autoregressive Exogenous-Neural Network (NARX-NN) was used in conjunction with the Bruun model and GIS methods to estimate the rate of sea-level rise, develop a coastal erosion model and coastal hazards maps, and simulate a sea-level increase with a maximum speed of 79.26 mm/year, and an average of about 25.34 mm/year, with a 1.48 m/year average erosion rate simulated from 2013 to 2020 along Merang kechil to Kuala Marang in Terengganu state coastal areas. According to the Bruun model, the areas most vulnerable to shoreline erosion are Kuala Nerus, Pendagan Buluh, and Kuala Ibai. Batu Rakit (Reach 1) has the highest rate of coastal erosion, at 28.16%, compared to 16.5 percent in Kuala Nerus (Reach 2) and 19.1% in Pengadang Bul ....
The impact of global warming presents an increased risk to the world’s shorelines. The Intergovernmental Panel on Climate Change (IPCC) reported that the twenty-first century experienced a severe global mean sea-level rise due to human-induced climate change. Therefore, coastal planners require reasonably accurate estimates of the rate of sea-level rise and the potential impacts, including extreme sea-level changes, floods, and shoreline erosion. Also, land loss as a result of disturbance of shoreline is of interest as it damages properties and infrastructure. Using a nonlinear autoregressive network with an exogenous input (NARX) model, this study attempted to simulate (1991 to 2012) and predict (2013–2020) sea-level change along Merang kechil to Kuala Marang in Terengganu state shoreline areas. The simulation results show a rising trend with a maximum rate of 28.73 mm/year and an average of about 8.81 mm/year. In comparison, the prediction results show a rising sea level with a m ....