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Use of different approaches to model catch per unit effort (CPUE) abundance of fish

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Title Use of different approaches to model catch per unit effort (CPUE) abundance of fish
 
Creator Yadav, Vinod K.
Jahageerdar, Shrinivas
Ramasubramanian, V.
Bharti, Vidya S.
Adinarayana, J.
 
Subject Logistic regression
Classification and Regression Tree
Neural networks
Catch Per Unit Effort
 
Description 1677-1687
Fish catch rates are expressed as Catch Per Unit Effort (CPUE) which is a performance index representing the success of fishing from commercial fishery statistics. A three-way comparison of prediction accuracy involving Logistic Regression(LR), Multi-Layer Perceptron (MLP) Neural Networks(NNs) and Classification And Regression Tree (CART) models was performed using a binary dependent variable (CPUE abundance as low or high) and a set of continuous and categorical predictor variables describing seasons, latitude, longitude, gear type, fishing hours and chlorophyll-a concentration. A dataset on CPUE abundance of the Gujarat coastal region during December 2007 to December 2009 was obtained. Overall accuracy (Correct Classification Rate) from NNs and CART models on training were 0.75 and 0.75, respectively and on test data they were 0.73 and 0.67, respectively while by LR they were 0.68 and 0.56 on training and test data, respectively. Present study infers that neither NNs nor CART model showed clear advantage of one over the other. This case study supports the need to test CPUE abundance models with independent data, and to use a range of criteria in assessing model performance. However, the preliminary CPUE Prediction requires multi or related variables in spatio-temporal mode for better CPUE predictions.
 
Date 2017-02-21T06:28:04Z
2017-02-21T06:28:04Z
2016-12
 
Type Article
 
Identifier 0975-1033 (Online); 0379-5136 (Print)
http://nopr.niscair.res.in/handle/123456789/40547
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJMS Vol.45(12) [December 2016]