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Title: Use of different modeling approach for sensitivity analysis in predicting the Catch per Unit Effort (CPUE) of fish
Authors: Yadav, V K
Jahageerdar, S
Adinarayana, J
Keywords: Artificial Neural Networks;Catch Per Unit Effort;Generalized Additive Model;Generalised Linear Model;Relative importance;Sensitivity analysis
Issue Date: Nov-2020
Publisher: NISCAIR-CSIR, India
Abstract: The contribution (Sensitivity analysis) of four variables, namely chlorophyll-a (Chl-a), sea surface temperature (SST), photosynthetically active radiation (PAR) and diffuse attenuation coefficient (Kd_490 or Kd) in predicting the Catch per Unit Effort (CPUE) of fish was evaluated using simple General Linear Model, Generalized Linear Model (GLM), Generalized Additive Model (GAM) and different explanatory methods of Artificial Neural Networks (ANN) technique. The models were assessed for their accuracy in determining the relative importance of the four variables in predicting the CPUE. GAM was an improvement over the General Linear Model, while ANN was found better than GAM. The six explanatory methods which can give the relative contribution or importance of variables were compared using ANN modeling techniques: (i) Connection weights algorithm, (ii) Garson’s algorithm (iii) Partial derivatives (PaD) (iv) Profile method (v) Perturb method, and (vi) Classical stepwise (forward and backward) method. Our results showed that the PaD method, Profile method, Input perturbation (50 % noise), and Connection weight approaches were only consistent in identifying the two most important variables (Chlorophyll-a and Kd) in the network. The distribution of profile plot & partial derivative helped indirectly in finding the other three variables in decreasing order of importance (PAR > fishing hour > SST). It was observed that the significance (sensitivity) of independent variables under GAM and explanatory methods of ANN were similar.
Page(s): 1729-1741
ISSN: 2582-6727 (Online); 2582-6506 (Print)
Appears in Collections:IJMS Vol.49(11) [November 2020]

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