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Sea levels are rising across the globe, and scientists warn that the pace will only accelerate in the coming decades. For Malaysia—home to a tropical climate, rapid urbanisation, and vast stretches of low-lying coast—this presents a serious challenge. Protecting the nation’s shores has become not only a scientific priority but also a matter of safeguarding communities, economies, and lives.

In recent years, Malaysian researchers have turned to machine learning (ML) to better understand and anticipate sea-level changes. A study by our team, published in Heliyon (DOI: 10.1016/j.heliyon.2023.e19426), demonstrated how advanced ML algorithms can successfully predict sea-level fluctuations at selected coastal locations across Malaysia. The findings show strong accuracy, underscoring the technology’s potential as a critical monitoring tool.

Another pressing issue in coastal science worldwide is sediment transport. The movement of sand and sediments—known as the sediment transport rate (STR)—is notoriously complex, especially when vegetation interactions are factored in. Our research, published in Environmental Science and Pollution Research (DOI: https://doi.org/10.1007/s11356-022-20472-y), investigated different ML models and found that some algorithms performed especially well in capturing these nonlinear relationships. Insights like these are vital for managing erosion and maintaining the health of fragile coastal ecosystems, challenges that are highly relevant for Malaysia’s shores too.

While much attention is placed on rising seas, tsunamis also pose a looming threat. Malaysia’s east coast, in particular, could be vulnerable to waves generated by earthquakes along the Manila Trench. To address this gap, our team simulated earthquake-induced tsunami scenarios of varying intensities. The study, published in Applied Water Science (DOI: https://doi.org/10.1007/s13201-022-01860-8), provides valuable insights that can guide mitigation strategies, evacuation planning, and policy responses—helping minimise potential losses of property and life.

Recently, our research team began an exciting international collaboration with Dr Pavitra Kumar1 of Manchester Metropolitan University and Dr Nicoletta Leonardi of the University of Liverpool. Together, we have proposed the development of a fully functional AI model capable of predicting flash floods in Malaysia’s coastal regions. This collaboration not only strengthens knowledge sharing between Malaysia and the UK but also opens the door to adopting cutting-edge coastal defence technologies.

Dr Kumar and Dr Leonardi have already pioneered a two-stage machine learning methodology for predicting coastal morphological changes (Water Resources Research, DOI: 10.1029/2024WR037523). Their approach combines a random forest classifier to categorise shoreline behaviour—eroding, accreting, stable, or fluctuating—with advanced deep learning models (LSTM and sequence-to-sequence) to forecast sediment volume changes. By applying this framework in Malaysia, we aim to adapt global innovations to local needs, giving decision-makers the tools to protect vulnerable communities.

In addition, we are collaborating with Prof. Ahmed El-Shafie2 on a project titled “AI-Powered Web Application for Predicting and Managing Long-Term Coastline Changes Along the United Arab Emirates.” Following the successful completion of this project, we plan to replicate and implement the findings in Malaysia. Prof. El-Shafie is one of the leading researchers in the application of artificial intelligence for water resources management and is affiliated with the National Water and Energy Center, United Arab Emirates University.

We believe that integrating machine learning and AI into coastal management is a powerful step forward. Supported by international partnerships and strengthened by local expertise, Malaysia is equipping itself to face tomorrow’s challenges—better prepared, better protected, and guided by cutting-edge research.
 

Associate Professor Ir Dr Ali Najah Ahmed
Faculty of Engineering and Technology
Email: @email 

Dr Hayana Dullah
Faculty of Engineering and Technology
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