TY  - EJOU
AU  - Yusaini, Qaisara Yusriena 
AU  - Muhamad, Muhammad Abdul Hakim 
AU  - Razali, Raiz 
AU  - Hasan, Rozaimi Che 
AU  - Said, Mohd Shahmy Mohd 
AU  - Zainal, Mohd Zainee Mohd 
AU  - Nuradi, Ikhsan 

TI  - Seabed Classification from Multi-Frequency Multibeam Data: A Study from Selorejo, Malang, Indonesia
T2  - Revue Internationale de Géomatique

PY  - 2025
VL  - 34
IS  - 1
SN  - 2116-7060

AB  - Sediment mapping is a crucial component of environmental science, particularly in the marine environment, where the analysis of seabed sediments is essential for various purposes, including marine resource management, habitat preservation, and infrastructure development. Sediment refers to the solid particles that are transported and deposited in different areas. Multibeam echosounders have revolutionized the field of seabed sediment mapping by providing unparalleled resolution and accuracy in seafloor surveys. This study aimed to produce sediment maps by implementing multi-frequency, e.g., 200, 400, 550, and 700 kHz multibeam data using a machine learning algorithm, e.g., Support Vector Machine (SVM) and Random Forest (RF) approach. The study area of this research was located at Waduk Selorejo, Kecamatan Ngantang, Kabupaten Malang, Indonesia. To achieve the aim, bathymetry and backscatter mosaic with multi-frequency results are produced. Then, the second derivatives from bathymetry and backscatter mosaic are produced (slope, ruggedness, aspect, curvature, mean, homogeneity, entropy, correlation, phi, and characterization) were used as the predictors. All the predictors with multi-frequency each have 0.5 m of spatial resolution. To classify, Pearson’s correlation coefficient method is used to identify strong and weak correlations. The weak correlation was used to run the classification by using SVM and RF. The Angular Range Analysis (ARA) characterization is used as the ground truth data. The accuracy assessment of the multiple-frequency sediment map will be compared with the two algorithms of machine learning (e.g., SVM and RF). This study highlights the importance of sediment mapping and the potential of acoustic methods and machine learning algorithms for seafloor classification.
KW  - Sediment mapping; multibeam echosounders; machine learning; seafloor classification

DO  - 10.32604/rig.2025.065284