Abstract
The effects of global warming are putting the world’s coasts at risk. Coastal planners need relatively accurate projections of the rate of sea-level rise and its possible consequences, such as extreme sea-level changes, flooding, and coastal erosion. The east coast of Peninsular Malaysia is vulnerable to sea-level change. The purpose of this study is to present an Artificial Neural Network (ANN) model to analyse sea-level change based on observed data of tide gauge, rainfall, sea level pressure, sea surface temperature, and wind. A Feed-forward Neural Network (FNN) approach was used on observed data from 1991 to 2012 to simulate and predict the sea level change until 2020 from five tide gauge stations in Kuala Terengganu along the East Coast of Malaysia. From 1991 to 2020, predictions estimate that sea level would increase at a pace of roughly 4.60 mm/year on average, with a rate of 2.05 ± 7.16 mm on the East Coast of Peninsular Malaysia. This study shows that Peninsular Malaysia’s East Coast is vulnerable to sea-level rise, particularly at Kula Terengganu, Terengganu state, with a rate of 1.38 ± 7.59 mm/year, and Tanjung Gelang, Pahang state, with a rate of 1.87 ± 7.33 mm/year. As a result, strategies and planning for long-term adaptation are needed to control potential consequences. Our research provides crucial information for decision-makers seeking to protect coastal cities from the risks of rising sea levels.
Highlights
Because the human population in coastal areas is growing, nearly 70% of the world’s beaches are retreating
We offer the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R), Total Squared Error (SSE), and Mean Square Error (MSE) that provide insights into the estimation capabilities of the model [61,62]
Bahru in the south of Peninsular Malaysia has a value with a rate of 2.59 ± 7.00 mm/year
Summary
Because the human population in coastal areas is growing, nearly 70% of the world’s beaches are retreating. More than 200 million people living within one meter of the mean sea level will be most directly affected by changes in the Global Sea. Level (GSL) [3,4,5]. Climate change and global warming are a threat to the environment as well as the environment’s primary users, most people. As the earth warms due to rising temperatures, the latter occurs [6]. Natural processes such as heavy rainfall, sea-level rise, erosion, flood, and human activity constantly affect the structure and morphologic characteristics of coastal city zones [7]
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