Elyas Soleimani , Moslem Daliri , Ali Salarpouri , Hossein Zamani
{"title":"Prediction of sardine and anchovy catches by double-boat purse seiners in the northern Persian Gulf using machine learning models","authors":"Elyas Soleimani , Moslem Daliri , Ali Salarpouri , Hossein Zamani","doi":"10.1016/j.dsr2.2025.105502","DOIUrl":null,"url":null,"abstract":"<div><div>Enhancing the efficiency of small pelagic purse-seine fisheries is essential for promoting responsible fisheries management in the Persian Gulf. Therefore, this study forecasts the spatiotemporal catch variations of Sind sardinella (<em>Sardinella sindensis</em>) and Buccaneer anchovy (<em>Encrasicholina punctifer</em>) caught by double-boat purse seiners in the northern Persian Gulf, Qeshm Island. To achieve this, a dataset comprising fishing records from 314 purse seine operations, along with associated environmental parameters obtained from satellite imagery—including sea surface temperature (SST), chlorophyll-a concentration, photosynthetically active radiation (PAR), wind speed, wind direction, depth, and distance—was compiled and analyzed using an advanced machine learning methodology covering the period from September 2014 to October 2023. The evaluation of the regression models used to predict sardine and anchovy catches—including Random Forest (RF), Boosting, and Support Vector Regression (SVR)—revealed varying levels of predictive performance across both species and model types. In the case of sardine, the Boosting Regression model yielded the highest predictive accuracy, characterized by a relatively low error (RMSE = 395.5) and moderate explanatory power (R<sup>2</sup> = 0.41). Conversely, for anchovies, the SVR model with a radial basis function (RBF) kernel demonstrated superior performance relative to the other models, with an RMSE of 437 and an R<sup>2</sup> of 0.35. The results suggest that anchovy catch prediction was more challenging and potentially influenced by additional unmodeled variables. The CPUE of sardine increases with rising chlorophyll-a concentrations up to approximately 2 mg/m<sup>3</sup>, but declines beyond this point. The optimal SST range was between 22 °C and 26 °C, whereas sardine catches declined at temperatures exceeding 30 °C. Because anchovy was consistently present across all sampling sets, distance from the shoreline emerged as the most influential parameter contributing to successful net captures. A negative relationship was observed between this factor and anchovy CPUE. As the second most important variable, the optimal SST range for anchovy was similar to that of sardine. Given the substantial fishing effort in the northern Persian Gulf, the findings of this study may help enhance regional fishing strategies by promoting the integration of climate change considerations into operational planning.</div></div>","PeriodicalId":11120,"journal":{"name":"Deep-sea Research Part Ii-topical Studies in Oceanography","volume":"222 ","pages":"Article 105502"},"PeriodicalIF":3.0000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Deep-sea Research Part Ii-topical Studies in Oceanography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967064525000517","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
Enhancing the efficiency of small pelagic purse-seine fisheries is essential for promoting responsible fisheries management in the Persian Gulf. Therefore, this study forecasts the spatiotemporal catch variations of Sind sardinella (Sardinella sindensis) and Buccaneer anchovy (Encrasicholina punctifer) caught by double-boat purse seiners in the northern Persian Gulf, Qeshm Island. To achieve this, a dataset comprising fishing records from 314 purse seine operations, along with associated environmental parameters obtained from satellite imagery—including sea surface temperature (SST), chlorophyll-a concentration, photosynthetically active radiation (PAR), wind speed, wind direction, depth, and distance—was compiled and analyzed using an advanced machine learning methodology covering the period from September 2014 to October 2023. The evaluation of the regression models used to predict sardine and anchovy catches—including Random Forest (RF), Boosting, and Support Vector Regression (SVR)—revealed varying levels of predictive performance across both species and model types. In the case of sardine, the Boosting Regression model yielded the highest predictive accuracy, characterized by a relatively low error (RMSE = 395.5) and moderate explanatory power (R2 = 0.41). Conversely, for anchovies, the SVR model with a radial basis function (RBF) kernel demonstrated superior performance relative to the other models, with an RMSE of 437 and an R2 of 0.35. The results suggest that anchovy catch prediction was more challenging and potentially influenced by additional unmodeled variables. The CPUE of sardine increases with rising chlorophyll-a concentrations up to approximately 2 mg/m3, but declines beyond this point. The optimal SST range was between 22 °C and 26 °C, whereas sardine catches declined at temperatures exceeding 30 °C. Because anchovy was consistently present across all sampling sets, distance from the shoreline emerged as the most influential parameter contributing to successful net captures. A negative relationship was observed between this factor and anchovy CPUE. As the second most important variable, the optimal SST range for anchovy was similar to that of sardine. Given the substantial fishing effort in the northern Persian Gulf, the findings of this study may help enhance regional fishing strategies by promoting the integration of climate change considerations into operational planning.
期刊介绍:
Deep-Sea Research Part II: Topical Studies in Oceanography publishes topical issues from the many international and interdisciplinary projects which are undertaken in oceanography. Besides these special issues from projects, the journal publishes collections of papers presented at conferences. The special issues regularly have electronic annexes of non-text material (numerical data, images, images, video, etc.) which are published with the special issues in ScienceDirect. Deep-Sea Research Part II was split off as a separate journal devoted to topical issues in 1993. Its companion journal Deep-Sea Research Part I: Oceanographic Research Papers, publishes the regular research papers in this area.