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Volume 12 - Summer and Autumn 2019                   ijmt 2019, 12 - Summer and Autumn 2019: 9-23 | Back to browse issues page

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Rezaee M, Golshani A, Mousavizadegan H. A New Methodology to Analysis and Predict Shoreline Changes Due to Human Interventions (Case Study: Javad Al-Aemmeh port, Iran). ijmt 2019; 12 :9-23
URL: http://ijmt.ir/article-1-654-en.html
A New Methodology to Analysis and Predict Shoreline Changes Due to Human Interventions (Case Study: Javad Al-Aemmeh port, Iran)
Meysam Rezaee1 , Aliasghar Golshani * 2, Hosein Mousavizadegan1
1- Department of Maritime Engineering, Amirkabir University of Technology, Tehran, Iran.
2- Department of Civil Engineering, Tehran Azad University, Central Branch, Tehran, Iran.
Abstract:   (8179 Views)
In recent years, determining the rate of shoreline change by its historical trend has been reported frequently. This study has focused on shorelines at the adjacency of Javad Al-Aemmeh port which has undergone successive constructions in its region. The decadal trend of studied shoreline change was determined by the historical trend method. A numerical method was also employed to reduce the probable deficiencies concerned to these constructions. Accordingly, for the first time, a framework was developed to compare the results of historical trend and numerical methods with a field-measured value both spatially and quantitatively and based on this comparison, the most suitable rate of change was assigned to each coastal landform. Finally, it was revealed that among the computed rates, the Linear Regression Rate (LRR) from historical trend method has given the best estimation for the shoreline change rate, but in those parts which the shoreline was directly under influence of human interventions the change rate derived from the numerical method has been more accurate. Besides, results showed that at those parts which the Net Shoreline Movement (NSM) and the Shoreline Change Envelope (SCE) are identical, predicting the future position of shoreline by its past trend is more reliable.
Keywords: Historical trend, Digital Shoreline Analysis System (DSAS), LITLINE, End Point Rate (EPR), Linear Regression Rate (LRR)
Full-Text [PDF 1048 kb]   (1760 Downloads)    
Type of Study: Research Paper |
Received: 2019/03/8 | Accepted: 2019/07/2
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