{"title":"Machine-learning and thresholding algorithms to automatically predict fishing effort of small-scale trawl fishery","authors":"","doi":"10.1007/s12562-023-01734-1","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>To assess fishery resources, it is necessary to easily obtain information on catch per unit effort, which is a resource indicator. In this study, two algorithms were developed for predicting the fishing effort (number of fishing operations, daily operating distance, and daily operating time) of a small-scale trawl fishery. These algorithms predict fishing efforts after preprocessing (including deleting outliers from the raw data), followed by classification of the operating conditions and threshold processing based on the operation period. One algorithm uses a machine-learning model for the classification process, and the other uses thresholding. The mean prediction error of the machine-learning algorithm on three datasets ranged from 1% to 11%, 2% to 8%, and 1% to 5% in terms of the number of operations, operating time, and operating distance, whereas that of the thresholding algorithm ranged from 3% to 52%, 2% to 5%, and 2% to 7%, respectively. A sensitivity analysis of the amount of training data indicated that prediction was possible using 5 days of training data. The developed algorithms are potentially useful for fish stock assessment.</p>","PeriodicalId":12231,"journal":{"name":"Fisheries Science","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fisheries Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s12562-023-01734-1","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"FISHERIES","Score":null,"Total":0}
引用次数: 0
Abstract
To assess fishery resources, it is necessary to easily obtain information on catch per unit effort, which is a resource indicator. In this study, two algorithms were developed for predicting the fishing effort (number of fishing operations, daily operating distance, and daily operating time) of a small-scale trawl fishery. These algorithms predict fishing efforts after preprocessing (including deleting outliers from the raw data), followed by classification of the operating conditions and threshold processing based on the operation period. One algorithm uses a machine-learning model for the classification process, and the other uses thresholding. The mean prediction error of the machine-learning algorithm on three datasets ranged from 1% to 11%, 2% to 8%, and 1% to 5% in terms of the number of operations, operating time, and operating distance, whereas that of the thresholding algorithm ranged from 3% to 52%, 2% to 5%, and 2% to 7%, respectively. A sensitivity analysis of the amount of training data indicated that prediction was possible using 5 days of training data. The developed algorithms are potentially useful for fish stock assessment.
期刊介绍：
Fisheries Science is the official journal of the Japanese Society of Fisheries Science, which was established in 1932. Recognized as a leading journal in its field, Fisheries Science is respected internationally for the publication of basic and applied research articles in a broad range of subject areas relevant to fisheries science. All articles are peer-reviewed by at least two experts in the field of the submitted paper. Published six times per year, Fisheries Science includes about 120 articles per volume. It has a rich history of publishing quality papers in fisheries, biology, aquaculture, environment, chemistry and biochemistry, food science and technology, and Social Science.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
