Fish HIS (habitat suitability index) modeling method based on SVM (support vector machine)

Abstract

The invention relates to a fish HIS (habitat suitability index) modeling method based on an SVM (support vector machine). The method comprises the following steps that 1, the suitability index value SI corresponding to a group of ocean factors is solved by combining various ocean environment factors; 2, all ocean environment factors and the SI are subjected to normalization; 3, an SVM HSI model is built on the basis of data in the second step; 4, different regions are predicated by the SVM HIS, the predicated root-mean-square errors and the correlation coefficient are obtained, and the fishery fishing condition is forecasted in GIS (geographic information system) environment; 5, simulation results are output and stored. Compared with the prior art, the fish HIS modeling method has the advantages that when the sample quantity is smaller, rules in the data can be better mined, the more reasonable HIS model and the fishing condition forecasting result can be obtained, and the like.

Description

technical field [0001] The invention relates to a fish habitat index modeling method, in particular to a fish habitat suitability index modeling method based on a support vector machine. Background technique [0002] The Habitat Suitability Index (HSI) model was first proposed by American scientists in the 1980s to simulate the response of organisms to the surrounding habitat elements. At present, it has been widely used in the fields of species distribution and management, and has gradually been applied in the analysis and prediction of fish fisheries, and achieved good results. The research of HSI is not only to theoretically understand the relationship between fishery distribution and marine environmental elements, but also to provide information reference for fishing production and fishery resource managers. The HSI model has been applied to fishery analysis of various fish species, such as bigeye tuna in the Indian Ocean, squid in the Atlantic and Pacific Oceans, macke...

AI Technical Summary

Problems solved by technology

[0003] There is a dynamic interactive relationship between marine environmental elements and the central fishery and constitutes a complex system. There is usually a certain correlation between environmental elements. However, traditional mathematical statistical methods cannot eliminate the inherent multiple correlations of environmental elements when constructing an HSI model. , the accuracy of analysis and prediction is greatly limited

At the same time, due to the limitation of commercial fishing data, the number of sample points of fishery resource data is very limited to a large extent, and it is difficult to obtain effective models from it by ordinary methods

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