2020美赛建模F题一等奖论文.pdf-资料库

Team # 2003376 Page 1 of 24 Problem Chosen F 2020 MCM/ICM Summary Sheet Team Control Number 2003376 Research on Prediction and Optimization of EDPs Problems Based on Human Rights and Cultural Protection Summary With the disappearance of some island nations caused by the rise of sea level, these nationals will face the main problems of relocating and risk of losing cultural on EDPs. From the perspective of human rights and cultural preservation, integrating the idea of global integration, the models on the prediction of EDPs’ number, the assessment of the risk of cultural loss, and the optimization of EDPs’ resettlement based on human rights and cultural protection were established and solved to obtain an optimal plan of EDPs resettlement by using theories and methodologies of mathematics, computers, climatology etc. Moreover, the policy recommendations for the United Nations to resolve EDPs issues were also proposed. For task 1-A, a prediction model of EDPs’ number was established to predict the quantitative relationship of EDPs over time in the next 60 years. It was obtained by setting and solving the prediction models of increment of sea level and the inundated areas based on the digital elevation map analysis by ArcGIS software. For task 1-B, a model for identifying and assessing the risk of cultural loss was established. It was solved by calculating the weights of the risk factors of cultural loss through AHP and the method of calculating the risk of cultural loss and its influencing factors presented by us. Its results show that their national culture will gradually lose over the years. For task 2, on the basis of constructing a Moore parity cellular automaton model of cultural state evolution, we formulated the cultural assimilation rules based on different policies and the life game model. The process of cultural assimilation will be under or not policy protection was simulated. Based on the findings of simulating, policies for addressing human rights and cultural protection of EDPs were proposed. For task 3, based on measuring the receiving country’s ability to receive EDPs and it facing the crisis, a multi-objective optimization model was established to get the optimal programming to relocate EDPs about the protection of human rights and culture. For task 4, two improved models were proposed. One is a fairness-based emergency optimization model for the country’s government accepting EDPs to formulate a policy of responding to the EDPs crisis based on the proportion of green-house gasses. The other is an evaluation model of idea willingness about the EDPs and the residents in their host country. For task 5, based on the previous research, the importance of policy implementation was discussed, and suggestions were made. Key words: EDPs; Sea level rise; Cultural loss risk; EDPs relocating optimization; Life game mode

Team # 2003376 Page 2 of 24 Content 1.Introduction .............................................................................................................................................. 3 1.1 Background ............................................................................................................................ 3 1.2 Restatement of Problems ........................................................................................................ 3 2. Assumptions and Symbol Table .............................................................................................................. 3 2.1 Assumptions ........................................................................................................................... 3 2.2 Symbols and Definitions ........................................................................................................ 4 3 Models and results .................................................................................................................................... 5 3 .1 Task1-A EDP number prediction model ................................................................................ 5 3.1.1 Modeling ideas ........................................................................................................... 5 3.1.2 Establishment of EDP Prediction Model .................................................................... 5 3.1.3 EDP Population Prediction Model and its Results and Analysis ................................ 7 3.2 Task1-B Evaluation model of EDP culture loss risk .............................................................. 8 3.2.1 Modeling ideas ........................................................................................................... 8 3.2.2 Identification and assessment of cultural loss risk ..................................................... 8 3.2.3 Solution and result of the model............................................................................... 10 3.3 Task2 Cultural protection model based on different policies ................................................ 11 3.3.1 Modeling ideas ......................................................................................................... 11 3.3.2 Modeling .................................................................................................................. 11 3.3.3 Solution of the model and its results and policy recommendation ........................... 12 3.4 Task3 An optimization model of refugee resettlement based on the protection ................... 14 3.4.1 Modeling ideas ......................................................................................................... 14 3.4.2 Establishment of a Metric Model for Receiving Refugees....................................... 14 3.4.3 The establishment of crisis measurement model in refugee receiving countries...... 15 3.4.4 Establishment of a bi-objective refugee resettlement optimization model based on protection of Human Rights and culture ................................................................................... 16 3.4.5 Solution and results of optimization model .............................................................. 17 3.5 Task4 Modeling and solving of design or improvement policies ......................................... 18 3.5.1 Idea of improvement ................................................................................................ 18 3.5.2 The established optimization model measures the insufficient analysis of the policy impact ........................................................................................................................................ 18 3.5.3 Establishment and solution of emergency model based on fairness ......................... 19 3.5.4 Establishing a Model Based on the Policy of the Emigration Country to Cope with the Refugee Crisis ........................................................................................................................... 20 3.6 Task5 the importance of implementing proposed policies ................................................... 20 4 Sensitivity Analysis ................................................................................................................................. 21 5 Conclusion ............................................................................................................................................... 21 5.1 Strengths ............................................................................................................................... 21 5.2 Weaknesses ........................................................................................................................... 22 6 Policy recommendation letter to UN Secretary General ..................................................................... 22 7.References ............................................................................................................................................... 23 8 Appendix ................................................................................................................................................. 23

Team # 2003376 Page 3 of 24 1.Introduction 1.1 Background There are several island nations such as The Maldives, Tuvalu, Kiribati, and The Marshal Island, as being at risk of completely disappearing due to rising sea levels. When its nation’s land disappears, not only do these environmentally displaced persons (EDPs) need to relocate, but there is also risk of losing a unique culture, language and way of life. In a very recent ruling, the UN has recognized that some EDPs might qualify as refugees[1]. Although a ruling has now been made, there is not yet a vision on how the international community should respond as these situations increase in magnitude and frequency[2]. Up to now, the UNHCR, in collaboration with other aid organizations, work to provide aid and assistance to refugees until they are resettled in another country. Become naturalized by their host state, or repatriate to their country of origin. 1.2 Restatement of Problems We were hired by the International Climate Migration Foundation (ICM-F) for climate migration to advise the UN, by developing a model and using it to analyze this multifaceted issue of when, why, and how the UN should step into a role of addressing the increasing challenge of EDPs. For task 1, we are supposed to predict how many people will become EDPs now and in the future, and then to analysis the risk of cultural loss, looking for factors that affect it. For task 2, we are supposed to propose policies to address EDPs in terms of both human rights (being able to resettle and participate fully in life in their new home) and cultural preservation. For task 3, we are supposed to build a model used to measure the potential impact of proposed policies. For task 4, we are supposed to explain how our models were used to design and/or improve our proposed policies. For task 5, an explanation, backed by your analysis, of the importance of implementing our proposed policies. 2. Assumptions and Symbol Table 2.1 Assumptions To simplify the problem, we make the following basic assumptions which is justified. Assuming no impact of natural disasters from ocean waves. Assuming irreversible after sea level rise. Assuming that the sea water flooding is a static over the process, and Seawater submergence is the only source of flooding. Assuming that the national population is evenly distributed by region.

Team # 2003376 Page 4 of 24 Supposing climate change only caused by E the DP generated. Assuming that all people in the flooded area become EDP. Assuming that when sea-level rise floods 80 % of a country's territory, that country is uninhabitable and will migrate in its entirety. Supposing that only the four countries of Maldives, Tuvalu, Kiribati, and Marshall Islands generate EDP. Assuming that the receiving countries of EDP are the top ten countries with global greenhouse gas emissions. 2.2 Symbols and Definitions Symbol that we mainly use in the model are shown in the following table: Symbols Definitions Table 1: Symbols and definitions Sea level rise in the country (cm) Change in average temperature in the country Population of the country Land area of the country ( ) Population density of the country Population in danger at the country Risk of cultural loss in the country The impact factor weight for the island nation's cultural loss Risk of the impact factor of the island nation's cultural loss Receiving capacity indicator of receiving country Number of refugees that can be resettled in receiving country Table 2: Countries in danger of disappearing i 1 2 3 4 country Maldives Tuvalu Kiribati Marshall Islands Table 3: Top 10 receiving countries for greenhouse gas emissions j 1 2 3 4 5 6 7 country China America India Russia Japan Germany Iran 8 Saudi Arabia 9 10 Korea Canada iSlRthiiTthiiPthiiKthi2kmtithiiNthiiRthi(),ikwkththi(),ikrkththijEthjjxthj

Team # 2003376 Page 5 of 24 3 Models and results 3 .1 Task1-A EDP number prediction model 3.1.1 Modeling ideas Tuvalu, Kiribati, Marshall Islands, and Australia are all in Oceania, the factors affecting temperature of those are similar, so we use the Australian temperature forecast to replace the temperature changes in the three countries. we find the temperature predictions of Australia and Maldives through the World Meteorological Organization's National Profile Database (WMO) firstly. we build a prediction model of sea level rise and use this model to predict the sea level rise of the four countries in the next 60 years secondly. Then, using the elevation digital models of the four countries, we find the area being submerged by the sea level rise in their coastal areas, finally, the submerged area was used to predict the number of EDP at risk. 3.1.2 Establishment of EDP Prediction Model ● Sea level rise prediction model The most important factors affecting sea level rise are the expansion of seawater and melting of glaciers caused by rising temperatures. Temperature prediction involves many complicated factors, many institutions have developed large-scale simulation software for this purpose. The Greenland ice sheet is in the process of melting due to global warming. Therefore, the issue of sea level rise is studied below based on the results of the World Meteorological Organization's temperature prediction and the mass balance of the Greenland ice sheet [1]. Step1 Thermal expansion model of seawater Let represent the sea level rise caused by thermal expansion (in cm), represent the amount of change in the average temperature of the country, whose possible value is 3 represent the thermal diffusion coefficient of the ocean, then: (1) Step2 Greenland Ice Sheet Mass Balance Model First, simplified the ice sheet into a rectangular parallelepiped, whose length is L, width is D, and height (thick ice layer) is . The LD is the surface area of the glacier. The mass[1] of this ablation process can be expressed as: Step3 Mass balance model and sea level rise (2) Combining the accumulation and ablation processes, we can derive the total mass balance model as follows: (3) iexSLRiTthi1k0.221i16.89=exiSLRTkh22()()()=−+−=−abMhsLDskLDhkLD20.025()=−=−−acabMMMLDhkLD

Team # 2003376 Page 6 of 24 About , (4) Sea Level Rise is the result of both thermal expansion and the mass balance of the Ice Sheet, so the formula for calculating sea level rise is: (5) ● Prediction model of territory submergence area based on digital elevation analysis map We consider the water as passive submergence [3]. Passive submergence is that a place is submerged if the water level is greater than a given elevation. Firstly, we use ArcGIS to build the elevation analysis map of four countries, and then use it to predict the area of those four. Figure 1 below shows our idea, calculated the area of the country to be inundated by sea water size as . Figure 1 Model of sea level rising inundated area ● Establishment of EDP population prediction model assuming the population of country is evenly distributed, the density per unit area of population , is the population of country i, is the land area of Country i The formula for predicting the number of people at risk in the country is: (6) (7) 360 1increasmentof sea levelGtwatermm= 1××360mbiicemmSLRMGt=imbiexiSlRSLRSLR=+ithiBiii=iiPMiPiMthiiNiiiNB=

Team # 2003376 Page 7 of 24 is the area of country that was submerged by the sea. 3.1.3 EDP Population Prediction Model and its Results and Analysis ● The prediction and calculation of sea level rise and its results Based on the sea level rise model, predicted value of the World Meteorological Organization were used as the projected temperatures of the four regions. Then, we can get sea level rise results for the four countries over the next 60 years calculated by a program executed by Matlab. These results show in table 4 and 5 below: Table 4. Sea level rise prediction near Tuvalu, Kiribati, Marshall Islands year Temperature(℃) Sea level rise (cm ) 2020 21.48 — 2030 2040 21.9 7.88 22.71 15.91 2050 23.87 2060 25.1 24.8 33.74 2070 26.57 42.81 2080 28.13 52.27 Table 5. Sea level rise prediction near Maldives Year 2020 2030 2040 Temperature(℃) 27.63 28.07 0.9 2050 30.2 2060 31.53 Sea level rise (cm ) — 8.42 16.99 26.03 35.6 2070 33.15 45.27 2080 34.85 57.64 ● The prediction and calculation of the submerged area of the territory and its results Since most of the four countries are island countries rounded by sea water, their elevation maps are not visible to the naked eye, so we use the enlarged island of Nanumanga in Tuvalu as an example of an elevation map model, as shown in figure 2. Once the elevation analysis maps of four countries are established, the relationship between sea level rise and the area of submerged area can be predicted by ArcGIS software, shown in figure 3: Figure 2. Elevation map of nanumanga Figure 3. Curve of sea level rise height ● Prediction and calculation of EDP population and its results and analysis With the mode, we can obtain the number of people facing EDP risk in four countries ， and inundated area iBith

Team # 2003376 Page 8 of 24 as an example, which are shown in table 6, attachment table 1-3 and figure 4: Table 6 The number of people at risk in Tuvalu over the next 60 years Year Sea level rise (cm ) Submerged area( km² ) The number of EDP 2030 7.88 2.88 2000 2040 15.91 3.76 4611 2050 24.8 4.43 7688 2060 33.74 5.32 10837 2070 42.81 6.23 10837 2080 52.27 7.12 10837 Figure 4 Number of EDPS at risk in the next 60 years As can be seen from figure 4, the population of EDP in the four countries will increase year by year in the next 60 years, and all the people in the Marshall and Tuvalu Countries will become environmental refugees in the next 60 years. According to the forecast, the island government should make the corresponding relocation planning, and formulate corresponding measures to protect EDP. 3.2 Task1-B Evaluation model of EDP culture loss risk 3.2.1 Modeling ideas Consulting the literature, we can find out the cultural composition and the value of the four island countries, confirm the influencing factors of the risk of cultural loss caused by the relocation of climate refugees. Determining the weight of the factors contributing to the risk of cultural loss by the analytic hierarchy process (AHP) we can put forward the formula to calculate the risk degree of cultural loss. 3.2.2 Identification and assessment of cultural loss risk ● a model for identifying the risk of cultural loss From the point of view of its path, the risk of cultural loss is characterized by objectivity, complexity, dual-effect and controllability. Objectivity is due to climate change, such as sea level rise, which makes the islanders climate refugees, and some of their culture will be lost in whole or in part. Complexity refers to the diversity and dynamic development of cultural loss risk. Dual-effect refers to both cultural differences and cultural complementarities.

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