ARTÍCULO
TITULO

Predictive Modeling of Eastern Little Tuna (Euthynnus affinis) Catches in the Makassar Strait Using the Generalized Additive Model

Ajeng R. Puspita    
Mega L. Syamsuddin    
Subiyanto    
Fadli Syamsudin and Noir P. Purba    

Resumen

The Makassar Strait (MS) is characterized by water mass from the Pacific Ocean and is one of the ITF (Indonesia Throughflow) branches. It carries warm water masses from the Pacific Ocean to the Indian Ocean. This research aims to analyze the relationship between CPUE of Eastern Little Tuna (Euthynnus affinis) and oceanographic variables, likewise predict the fishing area using the Generalized Additive Model (GAM). The research method used is spatial and temporal analysis. The data was used from 2015 to 2020. The data processed were sea surface temperature, chlorophyll-a, salinity, currents, sea level as predictor variables, and Eastern Little Tuna production as a response. Eastern Little Tuna catch data were normalized into Catch per Unit Effort, while the oceanographic data were extracted using ArcGIS. Based on the results of the GAM model, it was found that the model with five variables is the most suitable predictive model, with 16.4% CDE. Salinity is the most influential parameter on the catch of Eastern Little Tuna with a significance value of <2.00 × 10-16 ***. The optimum value for SST is 30?31 °C, chlorophyll-a is 1?2 mg/m3, salinity is 29?30 ppt, current velocity is 0.3?0.5 m/s and sea level is between 0.6?0.7 m. Based on the GAM prediction results, a high CPUE value will be obtained in the southwest monsoon (March to May). Fishing activity carried out in the best season will implement the adoption of harvest control measures.

 Artículos similares

       
 
Jean-Marc Guarini and Jennifer Coston-Guarini    
In their 2023 book, ?The Blue Compendium: From Knowledge to Action for a Sustainable Ocean Economy?, Lubchenko and Haugan invoked alternate stable (AS) states marginally as an undesired consequence of sources of disturbance on populations, communities an... ver más

 
Rejath Jose, Faiz Syed, Anvin Thomas and Milan Toma    
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for... ver más
Revista: Applied Sciences

 
Nirmal Acharya, Padmaja Kar, Mustafa Ally and Jeffrey Soar    
Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental hea... ver más
Revista: Applied Sciences

 
Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar    
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term ave... ver más
Revista: Water

 
Tadas Tamo?iunas and ?arunas Skuodis    
The mechanical properties of pavement materials are crucial to the design and performance of flexible pavements. One of the most commonly used measures of these properties is the resilient modulus (Er). Many different models were developed to predict the... ver más
Revista: Infrastructures