A Model for Fishery Forecast based on Cluster Analysis and Nonlinear Regression

DOI

10.2991/aiie-15.2015.113How to use a DOI?

Keywords

pelagic fishing; fisheries forecasting; cluster analysis; nonlinear regression

Abstract

There has been an increasing amount of research in the relationship between environmental factors and fishing yield. This paper adds to the body of knowledge by developing a new model for forecasting fishing yield. The model combines fishery domain expert knowledge, marine environmental factor data such as water temperature, chlorophyll concentration and sea surface level as base data and applies cluster analysis that incorporates function fitting and nonlinear regression for data analysis and processing. The model is tested for forecast accuracy and the test result is compared with those using RBF and SVM, the two methods commonly used for similar purposes. The comparison result reveals this new model increases both the accuracy in fishery forecast and the reliability in guiding fishery production and related activities. It can also help explore and discover the distribution of fishing grounds.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - H.C. Yuan
AU  - M.X. Tan
AU  - Y. Chen
PY  - 2015/07
DA  - 2015/07
TI  - A Model for Fishery Forecast based on Cluster Analysis and Nonlinear Regression
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 415
EP  - 418
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.113
DO  - 10.2991/aiie-15.2015.113
ID  - Yuan2015/07
ER  -