Support vector machine classification method for predicting Jakarta bay bottom sediment type using multibeam echosounder data

Manik Henry Munandar, . and Solikin Steven, . and Sri Pujiyati, . and Susilohadi, . Support vector machine classification method for predicting Jakarta bay bottom sediment type using multibeam echosounder data. pp. 477-491. ISSN 2231-8526

The need for accurate seafloor maps is increasing along with the increase in marine activities such as dredging construction of buildings on the coast and offshore and navigation of ships to prevent shipwrecks while sailing. The hydroacoustic technology used in this study is the multibeam echosounder system (MBES) which is the most advance acoustic instrument today. MBES can sweep very large areas in a short time so that the survey costs can be reduced. The aim of this research was firstly to classify the seabed sediment in G-Island Jakarta Bay using supervised classification technique. Secondly to analyze the acoustic characteristic of the seabed sediment and compare it with the physical characteristic of the sediment.This research was conducted on October 31st to November 5th 2016 in the waters of G-Island Jakarta Bay. In this study supervised classification techniques were applied. The supervised classification techniques used in this research was Support Vector Machine (SVM). SVM produces classifications with 5 main classes namely clay fine silt medium silt coarse silt and fine sand. The overall accuracy value of the SVM method was 80.25 with the Kappa coefficient value of 0.2031 which is categorized into the fair class in its classification.

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Abstract

The need for accurate seafloor maps is increasing along with the increase in marine activities such as dredging construction of buildings on the coast and offshore and navigation of ships to prevent shipwrecks while sailing. The hydroacoustic technology used in this study is the multibeam echosounder system (MBES) which is the most advance acoustic instrument today. MBES can sweep very large areas in a short time so that the survey costs can be reduced. The aim of this research was firstly to classify the seabed sediment in G-Island Jakarta Bay using supervised classification technique. Secondly to analyze the acoustic characteristic of the seabed sediment and compare it with the physical characteristic of the sediment.This research was conducted on October 31st to November 5th 2016 in the waters of G-Island Jakarta Bay. In this study supervised classification techniques were applied. The supervised classification techniques used in this research was Support Vector Machine (SVM). SVM produces classifications with 5 main classes namely clay fine silt medium silt coarse silt and fine sand. The overall accuracy value of the SVM method was 80.25 with the Kappa coefficient value of 0.2031 which is categorized into the fair class in its classification.

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Item Type:	Article
AGROVOC Term:	Application methods
AGROVOC Term:	Application of technology
AGROVOC Term:	Coastal waters
AGROVOC Term:	Measuring instruments
AGROVOC Term:	Sedimentation (technology)
AGROVOC Term:	Classification systems
AGROVOC Term:	Topography
AGROVOC Term:	Data collection
AGROVOC Term:	Prediction
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/9369

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