2024年美赛A题例子

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2024 MCM/ICM Summary Sheet

Investigation of seawater temperature change influence

on Scotland fishery

Summary

Contents

1 Introduction

1.1 Outline

1.2 Main assumption

2 Variable Description 3 Task 1

3.1 BP neural network

3.1.1 preparation and information collection 3.1.2 EMD steps

3.1.3 Construction of BP neural network model based on EMD

3.1.4 Living place prediction

4 Task 2

5 Task 3

6 Task 4

7 Task 5

8 Sensitivity Analysis

9 Strength and weakness

1 Introduction

Fishery is on of the most essential and important industry of ocean industry. The ecological environment which fishes living at will directly impact the development of fishery. Ever since 90s of last century, natural environment has been fiercely damaged which result from the ignorance of environment protecting. Including the excessive emission of greenhouse gas and over-reliance on the fossil resources which led to the worldwide temperature raising. As a result, the temperature of sea surface was increasing simultaneously and many part of world became aware of its effects brought to fishery industry.

1.1 Outline

Our objectives is to investigate the habitat of herring and mackerel living in the Scotland sea area base on the condition of global sea temperature increasing. Then we predict the migration of fish as well as conducting the profit risk assesment for fishery industry in Scotland. To realize these objectives we will proceed as following steps:

· Establish a BP neural network to forecast temperature change in 50 years of Scotland sea area.

· Use GIS analyse to evaluate the influence of environment change on herring and mackerel.

· Implement a simulation method to compute the maximal and minimal time cost for herring and mackerel to migrate their activity position.

· Enact a more economically attractive and competitive strategy for domestic industry by the model made.

· Discuss the possible influence once proportional fishery entering other territorial sea area.

1.2 Main Assumption

Future prediction is always probabilistic, cause we cannot make any variables constant, especially when they are connected closely. So, it tends to be necessary to come up with several main assumptions for our model. Also, some other assumptions are introduced in the remainder of this paper. · · · · ·