Hazard assessment and modeling of erosion and sea level rise under global climate change conditions for coastal city management

• Main Authors: Bagheri, Milad, Ibrahim, Zelina Z., Mansor, Shattri, Abd Manaf, Latifah, Akhir, M. F., Talaat, W. I. A. W., Wolf, Isabelle D.
• Format: Article
• Published: American Society of Civil Engineers (ASCE) 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/108149/; https://ascelibrary.org/doi/10.1061/%28ASCE%29NH.1527-6996.0000593
• Institution: Universiti Putra Malaysia

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 Buluh (Reach 3). The findings of this study might be utilized to build new coastal hazard erosion maps in a GIS framework, which could then be used as part of Malaysia’s East Coast zone vulnerability assessment. The findings may also aid in the prioritization of conservation efforts in afflicted areas or the decision to adapt to the effects of coastal erosion. This article presents a methodological framework and an erosion management prioritization system to help coastal managers, planners, and developers identify hazardous zones and improve coastal management plans using geospatial models.