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A model for fishery forecast based on cluster analysis and nonlinear regression
conference contribution
posted on 2023-05-23, 11:08 authored by Yuan, HC, Tan, MX, Gu, YT, Chen, YingThere 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.
History
Publication title
Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2015)Volume
123Editors
CJ LeePagination
415-418ISBN
978-1-5108-0645-0Department/School
School of Information and Communication TechnologyPublisher
Atlantis PressPlace of publication
Paris, FranceEvent title
2015 International Conference on Artificial Intelligence and Industrial Engineering (Aiie 2015)Event Venue
Phuket, ThailandDate of Event (Start Date)
2015-07-26Date of Event (End Date)
2015-07-27Rights statement
Copyright 2015 The authorsRepository Status
- Restricted