[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125125-en":3,"doc-seo-125125-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125125,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Improving machine learning predictions to estimate fishing effort using vessel’s tracking data - Ecological Informatics Article","Small-Scale Fisheries (SSF) make up over 80% of the global fleet and underpin many coastal livelihoods, yet they face multiple threats requiring better monitoring. Precise fishing-effort estimation from high-resolution spatio-temporal vessel tracking data supports mapping fishing intensity, protecting key grounds, signaling possible stock depletion, and guiding ecosystem-based management. This study evaluates supervised machine learning approaches and simple preprocessing and postprocessing strategies to improve classification accuracy across multiple fisheries.","Ecological Informatics 85 (2025) 102953  \nContents lists available at ScienceDirect  \nEcological Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ecolinf)[ www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf)  \n| Improving machine learning predictions to estimate fishing effort using vessel’s tracking data |  |  |  |\n| --- | --- | --- | --- |\n| J. Samar˜aoa,d, A. Moreno a, M.B. Gaspara,c, M.M. Rufino a,b,*\u003Cbr>a Portuguese Institute for the Sea and the Atmosphere (IPMA), Av. Dr. Alfredo Magalh˜aes Ramalho, 6, 1495-65 Lisboa, Portugal b Centre of Statistics and its Applications (CEAUL), Faculty of Sciences, University of Lisbon, Portugal\u003Cbr>c CCMAR\u003Cbr>d Nova School of Science and Technology (FCT), Almada, Portugal |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Fishing effort Machine leaning\u003Cbr>Spatio-temporal high-resolution data Small scale fisheries |  | Small-Scale Fisheries (SSF) comprise over 80 % of the global fleet and serve as the primary income source for numerous coastal communities. However, these critical fisheries face various threats. To effectively monitor SSF activities and their ecological impacts, it is required precise estimation of fishing effort using high-resolutionspatio-temporal data. This information can identify areas with high fishing density, warranting protection of their main fishing grounds against other users (i.e. ocean grabbing), while also signalling potential stock depletion requiring management interventions and preserving the ecosystems from which these fisheries depend on.\u003Cbr>In this study, we propose a series of steps to enhance the performance of Machine Learning algorithms in estimating fishing effort. We assessed seven supervised ML algorithms, including Logistic Regression, Ridge Classifier, Random Forest Classifier, K-Neighbours, Gradient Boosting Classifier, LinearSVC, Recurrent Neural Networks and XGBoost, using four case studies, from bivalve dredge and octopus pots and traps fisheries.\u003Cbr>First, in a preliminary statistical analysis between common error measures derived from the confusion matrix was decided to use accuracy, precision, and sensitivity as evaluation criteria. We found that a simple moving average applied to speed, employed as a pre-processing technique using ten neighbouring points, showed up to 3 % improvement in results. Random Forest and XGBoost gave the best performances among the models compared (18 % change), using the variables Latitude, Longitude, Speed, Time, and Month (accuracies near 99 %)(61 % change). The proportion of the training/test dataset, showed a minimal impact on accuracy, with changes of less than 8 % when varying the training data percentage between 10 % and 90 %, making 60 % a suitable compromise. Considering the sampling unit to be (1) point-based (randomly selected pings) or (2) boat trip-based (randomly selected boat trips), leaded to changes in accuracy between 2.53 % and 3.99 %, depending on the model. Temporal resolution (ping rate) showed minimal effects on model performance, ranging from less than 2 % for intervals between 30 s (raw data with irregular time series) to 10 min (regular time series). As a postprocessing step, it was concluded that replacing isolated data points with neighbouring values, significantly enhanced the detection of fishing events, with improvements ranging from 80 % to 250 %, depending on the model.\u003Cbr>In conclusion, this study presents a straightforward procedure for selecting a machine learning method and enhancing its power of classification using simple procedures. These approaches should be applied in all works using machine learning to produce fishing effort maps. |  |\n\n1. Introduction  \nGlobal fish production is projected to reach 200 million tons by 2029, placing immense pressure on marine ecosystems and the  \nsustainability of fish stocks. Robust, science-driven fisheries data are essential for developing effective management strateg","cbCaim0CVITv8OQY","https://ap.wps.com/l/cbCaim0CVITv8OQY","pdf",5772126,1,15,"English","en",105,"# Introduction\n# Methods and Machine Learning Approaches\n## Model evaluation and error measures\n## Preprocessing and postprocessing strategies\n# Results and Comparative Performance\n## Best-performing models and key variables\n## Sensitivity to dataset split and sampling design\n## Effects of temporal resolution\n# Conclusions","[{\"question\":\"Why is estimating fishing effort important for small-scale fisheries (SSF)?\",\"answer\":\"Estimating fishing effort helps monitor SSF activities and ecological impacts. It enables identifying high fishing density areas for protection, and supports management actions if potential stock depletion is detected.\"},{\"question\":\"Which supervised machine learning algorithms are assessed in the study?\",\"answer\":\"The study evaluates seven supervised ML algorithms, including Logistic Regression, Ridge Classifier, Random Forest Classifier, K-Neighbours, Gradient Boosting Classifier, LinearSVC, Recurrent Neural Networks, and XGBoost.\"},{\"question\":\"What preprocessing and postprocessing steps improve fishing-event detection?\",\"answer\":\"The study finds that applying a simple moving average to speed using neighboring points improves results, and that replacing isolated data points with neighboring values substantially enhances the detection of fishing events.\"}]","Improving machine learning predictions to estimate fishing effort using vessel’s tracking data - Ecological Informatics Article | PDF",1785896798,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improving-machine-learning-predictions-to-estimate-fishing-effort-using-vessels-tracking-data-ecological-informatics-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-machine-learning-predictions-to-estimate-fishing-effort-using-vessels-tracking-data-ecological-informatics-article/125125/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is estimating fishing effort important for small-scale fisheries (SSF)?","Question",{"text":75,"@type":76},"Estimating fishing effort helps monitor SSF activities and ecological impacts. It enables identifying high fishing density areas for protection, and supports management actions if potential stock depletion is detected.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised machine learning algorithms are assessed in the study?",{"text":80,"@type":76},"The study evaluates seven supervised ML algorithms, including Logistic Regression, Ridge Classifier, Random Forest Classifier, K-Neighbours, Gradient Boosting Classifier, LinearSVC, Recurrent Neural Networks, and XGBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"What preprocessing and postprocessing steps improve fishing-event detection?",{"text":84,"@type":76},"The study finds that applying a simple moving average to speed using neighboring points improves results, and that replacing isolated data points with neighboring values substantially enhances the detection of fishing events.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]