Open AccessPrediction of Fish School based on Neural Network and Gray Prediction
Author(s) -
MuZhen Cai,
Bo Dong,
Yexiang Xiao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/4/042081
Subject(s) - fishing , livelihood , artificial neural network , fishery , habitat , computer science , gray (unit) , fish <actinopterygii> , fishing industry , geography , environmental science , environmental resource management , artificial intelligence , ecology , medicine , archaeology , radiology , biology , agriculture
Rising ocean temperatures affect the distribution of fish. The livelihoods of many small Scottish fishing companies have also been affected. We designed a fish migration prediction model, a system that predicts fish migration and helps small fishing companies make decisions. First, we creatively proposed the neural network algorithm modified to establish habitat evaluation model based on previous environmental data. Then, the gray prediction algorithm is used to predict the environmental data of each water area in the next 50 years, and the best habitat location of each year is evaluated by the evaluation model based on the environmental data. The migration route of the fish can be obtained by summarizing the best habitats each year in chronological order. We use the knowledge of graph theory to establish a "fishing judgment model" to determine whether small fisheries companies can catch fish in their fishing range. Then we use a triple nested loop to establish a continuous time calculation model, and use this model to discuss the continuous fishing time of a fishing company located in a certain place from a certain year in the future