Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55698
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dc.contributor.authorYadav, V K-
dc.contributor.authorJahageerdar, S-
dc.contributor.authorAdinarayana, J-
dc.date.accessioned2020-11-25T06:03:29Z-
dc.date.available2020-11-25T06:03:29Z-
dc.date.issued2020-11-
dc.identifier.issn2582-6727 (Online); 2582-6506 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55698-
dc.description1729-1741en_US
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJMS Vol.49(11) [November 2020]en_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCatch Per Unit Efforten_US
dc.subjectGeneralized Additive Modelen_US
dc.subjectGeneralised Linear Modelen_US
dc.subjectRelative importanceen_US
dc.subjectSensitivity analysisen_US
dc.titleUse of different modeling approach for sensitivity analysis in predicting the Catch per Unit Effort (CPUE) of fishen_US
dc.typeArticleen_US
Appears in Collections:IJMS Vol.49(11) [November 2020]

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