亚太数学建模的c题论文，代码使用python实现

Team # apmcm2202706

Team Number :

apmcm2202706

Problem Chosen :

C

correlation tests. Finally, a proven method for alleviating global warming is proposed

based on our findings.

In the first part, to solve the problem of relatively small amount of data relative to

the global area, we propose a superposition prediction model based on Gaussian

distribution, which can use only a small amount of data to fit the global temperature

distribution and finally derive the global average temperature variation over time

based on the global temperature distribution in latitude and longitude. Next, our study

relies on the obtained global temperature data to train the BP and LSTM neural

networks separately, and the obtained model fits the historical temperature data

relatively accurately and predicts the global temperature change in the next 80 years.

global temperature to regional temperature and time through multiple regression

analysis. Fires, novel coronavirus outbreaks and forest fires were selected as proxies

to examine the impact of regional temperatures and natural hazards. Analysis of the

observations shows that natural hazards do not have a significant impact on regional

temperatures or global temperatures in the short or long term.

Next, a multiple regression analysis was conducted to study the variables that

have a significant impact on global temperature, in order to find the factors that have

the greatest impact on global temperature, so as to achieve the purpose of observation

and control of global temperature change.

In the end we wrote a non-technical paper to be submitted to the organizing

Team # apmcm2202706

1.3 Our Work ............................................................................................................................ 2

2 Assumptions and Justifications ...................................................................................................... 2

3 Notations ........................................................................................................................................ 3

4 Data Restoration ............................................................................................................................. 3

5 Models for question 1 .................................................................................................................... 4

5.1 The solution for part a) ........................................................................................................ 4

5.2 Neural Network model ........................................................................................................ 5

5.3 Gaussian Distribution model ............................................................................................. 11

7.1 Sensitivity of average annual temperature in the region ................................................... 17

7.2 Sensitivity of other factors ................................................................................................ 18

8 A non-technical article to APMCM organizing committee .......................................................... 18

9 Model Evaluation and Further Discussion ................................................................................... 19

9.1 Strengths ........................................................................................................................... 19

9.2 Weaknesses ....................................................................................................................... 19

9.3 Further Discussion ............................................................................................................ 19

10 Conclusion ................................................................................................................................. 20

References ....................................................................................................................................... 20

Appendices ...................................................................................................................................... 20

Team # apmcm2202706 Page 1 of 28

1 Introduction

50℃ in the Middle East, the reality that the earth is burning is beyond doubt.

accumulation of greenhouse effect, which leads to an imbalance between the energy

absorbed and emitted by the Earth's atmospheric system, and the accumulation of

energy in the Earth's atmospheric system, leading to an increase in temperature and

global warming [1].

changes. At the beginning, we have to make a judgement about whether the increase

of global temperature in March 2022 resulted in a larger increase than observed over

any previous 10-year period. Next, to realize how serious the dilemma has been, we

need to build several mathematical models which can not only reflect and demonstrate

the conditions of the past years and will also have the ability to predict and tackle the

future. After that, when it comes to the issue about which model work best, we shall

test our models through a prediction of whether the average global temperature will

reach 20℃ in 2050 or 2100.

Then we need to utilize the results from the first problem to construct a module to

evaluate some possible indicators to determine whether they are the causes of global

Team # apmcm2202706 Page 2 of 28

Figure 1: Flowchart of our modeling work

2 Assumptions and Justifications

1.The sensitivity of global warming to an area is only related to the average

annual temperature at that location, ignoring the remaining factors that are of little

relevance. What’s more, through subsequent model sensitivity analysis, we can

demonstrate that the effects of the remaining factors are white noise compared to the

mean temperature of the site and can be ignored.

Team # apmcm2202706 Page 3 of 28

3 Notations

forgotten Gate

memory cells with input and the weight matrix of the

input gate with weight matrix of hidden layers, memory