[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128019-en":3,"doc-seo-128019-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128019,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT BASED ON SCIENTIFIC ECHOSOUNDER DATA - APPLICATION OF MACHINE LEARNING ALGORITHMS","This study maps three main benthic habitats—coral, seagrass, and sand—in Kapota Atoll (Wakatobi, Indonesia) using a single-beam echosounder (SBES) Simrad EK15. Acoustic signals are processed in Sonar5-Pro, and eight acoustic parameters derived from depth, first-echo characteristics, and cumulative energy of the second and third echoes are used as inputs. Two machine learning methods, Random Forest and Support Vector Machine, are applied in XLSTAT Basic+. Results show that 49 acoustic-parameter combinations achieve mapping accuracy meeting the minimum standard (≥60%), with best performance reaching 79.33% (RF) and 78.67% (SVM).","Submitted: 2024-09-12 | Revised: 2024-11-13 | Accepted: 2024-11-20  \nKeywords: acoustic parameters, single-beam echosounder, mapping accuracy, Random Forest, Support Vector Machine  \nBaigo HAMUNA [0000-0002-0706-2496]* , Sri PUJIYATI [0000-0003-4049-4589]** , Jonson Lumban GAOL [0000-0001-8908-3161]**,  \nTotok HESTIRIANOTO [0000-0002-1636-4525]**  \nCLASSIFICATION AND PREDICTION OF BENTHIC HABITAT BASED ON SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING  \nALGORITHMS  \nAbstract  \nThis study aims to map three main benthic habitats (coral, seagrass, and sand) in Kapota Atoll (Wakatobi, Indonesia) using a single-beam echosounder (SBES) Simrad EK15. The acoustic data were processed using Sonar5-Pro software. Eight acoustic parameters were used as input for the classification and prediction of benthic habitats, including depth (D), five acoustic parameters of the first echo (BD, BP, AttSv1, DecSv1, andAttDecSv1), and cumulative energy of the second and third echoes (AttDecSv2 and AttDecSv3). The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. The study results show that 49 combinations of acoustic parameters produce benthic habitat maps that meet the minimum accuracy standards for benthic habitat mapping (≥60%). Using eight acoustic parameters produces a more accurate benthic habitat map than using only two main SBES parameters (DecSv1 and AttDecSv2 parameters or E1 and E2 in the RoxAnn system indicating the roughness and hardness indices). The RF and SVM algorithms produce benthic habitat maps with the highest accuracy of 79.33% and 78.67%, respectively.  \nEach acoustic parameter has a different importance for the classification of benthic habitats, where the order of importance of each acoustic parameter in the overall classification follows the following order: AttDecSv2 > D > DecSv1 > BD > AttDecSv3> AttSv1 > AttDecSv1 > BP. Overall, using more acoustic parameters can significantly improve the accuracy of benthic habitat maps.  \n* Cenderawasih University, Faculty of Mathematics and Natural Science, Department of Marine Science and Fisheries, Indonesia  \n** IPB University, Faculty of Fisheries and Marine Sciences, Department of Marine Sciences and Technology, Indonesia, [sripu@apps.ipb.ac.id](sripu@apps.ipb.ac.id)  \n1. INTRODUCTION  \nDevelopments in benthic habitat mapping have resulted in various approaches, data types, technologies, and models that can be used to understand and map the distribution patterns of biotic and abiotic components on the seafloor (Misiuk & Brown, 2024) . Various hydroacoustic instruments for seafloor habitat mapping have developed rapidly with varying degrees of effectiveness (Anderson et al., 2008; Brown et al., 2011; Pijanowski & Brown, 2022; Wölfl et al., 2019) . Scientific echosounders, such as single-beam echosounders (SBES), have been reported as reliable instruments for detecting objects in the water column (Manik et al., 2014; Moszynski & Hedgepeth, 2000; Pujiyati et al., 2022) and for classifying and mapping the seafloor (Henriques et al., 2015; Lee & Lin, 2018; McLaren et al., 2019; Reshitnyk et al., 2014; Sánchez-Carnero et al., 2023; Solikin et al., 2018; Vassallo et al., 2018) . SBES have become standard instruments in recent decades due to their affordable cost (Anderson et al., 2008; Fajaryanti & Kang, 2019; Sánchez-Carnero et al., 2023) and standard data processing procedures (Anderson et al., 2008) .  \nThe accuracy level in the classification process and spatial mapping of benthic habitats and seabed substrates is a fundamental problem. The selection of classification methods isan important factor in improving map accuracy (Shao et al., 2021) . Various studies have used multiple methods to classify and map seabed habitats or substrates from SBES instruments, such as Principal Component Analysis (PCA) (Bartholomä et al., 2020; Bravo & Grant, 2020; F","cbCaicJ6MhjY41pf","https://ap.wps.com/l/cbCaicJ6MhjY41pf","pdf",1014690,3,1,17,"English","en",105,"# INTRODUCTION\n## Benthic habitat mapping approaches and hydroacoustic instruments\n## Acoustic backscatter analysis and traditional statistics (PCA, clustering)\n## Machine learning classification from SBES data\n## Motivation: parameter roles and third-echo contribution\n## Research aims and workflow","[{\"question\":\"What habitats are classified in this study and where is the study conducted?\",\"answer\":\"The study classifies coral, seagrass, and sand in Kapota Atoll (Wakatobi, Indonesia).\"},{\"question\":\"Which acoustic data sources and parameters are used as inputs for machine learning?\",\"answer\":\"Data are collected with a single-beam echosounder (SBES) Simrad EK15 and processed in Sonar5-Pro. Eight parameters are used, including depth, first-echo parameters, and cumulative energy from the second and third echoes.\"},{\"question\":\"How do Random Forest and Support Vector Machine perform in predicting benthic habitats?\",\"answer\":\"Random Forest achieves the highest reported accuracy at 79.33%, while Support Vector Machine reaches 78.67% in the benthic habitat maps.\"}]","CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT BASED ON SCIENTIFIC ECHOSOUNDER DATA - APPLICATION OF MACHINE LEARNING ALGORITHMS | PDF",1785943966,43,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"classification-and-prediction-of-benthic-habitat-based-on-scientific-echosounder-data-application-of-machine-learning-algorithms","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/classification-and-prediction-of-benthic-habitat-based-on-scientific-echosounder-data-application-of-machine-learning-algorithms/128019/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What habitats are classified in this study and where is the study conducted?","Question",{"text":76,"@type":77},"The study classifies coral, seagrass, and sand in Kapota Atoll (Wakatobi, Indonesia).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which acoustic data sources and parameters are used as inputs for machine learning?",{"text":81,"@type":77},"Data are collected with a single-beam echosounder (SBES) Simrad EK15 and processed in Sonar5-Pro. Eight parameters are used, including depth, first-echo parameters, and cumulative energy from the second and third echoes.",{"name":83,"@type":74,"acceptedAnswer":84},"How do Random Forest and Support Vector Machine perform in predicting benthic habitats?",{"text":85,"@type":77},"Random Forest achieves the highest reported accuracy at 79.33%, while Support Vector Machine reaches 78.67% in the benthic habitat maps.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]