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Comparison between different modeling techniques for assessing the role of environmental variables in predicting the catches of major pelagic fishes off India’s north-west coast.

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Title Comparison between different modeling techniques for assessing the role of environmental variables in predicting the catches of major pelagic fishes off India’s north-west coast.
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Creator Not Available
 
Subject Artificial neural networks, Canonical correlation analysis, Fish resources, Generalized additive model, Generalised linear model, Sensitivity analysis
 
Description Not Available
The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM.
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Date 2023-05-16T04:08:53Z
2023-05-16T04:08:53Z
2022-02-01
 
Type Journal
 
Identifier Not Available
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http://krishi.icar.gov.in/jspui/handle/123456789/77415
 
Language English
 
Relation Not Available;
 
Publisher ResearchGate