MODELS & DATABASE Instant Notes The ...

MODELS & DATABASE Instant Notes

The mathematical methods and the data resource for ecological and environmental studies

Presentation by ‘Joey’ Zhouyuan Li RG | G | in | f | t

2017-12-05 / WUR, Wageningen, Netherland (Resource Ecology Group) 2017-10-18 / CAS, Xiamen, China (prof. Weiqiang Chen Group) 2017-10-12 / APU, Beppu, Japan (prof. Yan Li Group)

MODELS & DATABASE Instant Notes

The mathematical methods and the data resource for ecological and environmental studies

Presentation by ‘Joey’ Zhouyuan Li RG | G | in | f | t

To the Xuehua’s Ecology Group (903 / 905, School of Environment) Tsinghua University To prof. dr. ‘Jake’ Jiang Zhang

Table of Content About Why to categorize (Chinese Introduction pages 中文简介)

4 main types models

MODELS & DATABASE Instant Notes

The mathematical methods and the data resource for ecological and environmental studies

1.1 Dynamic 1.2 Complexity 2.1 Statistic 2.2 Machine learning 3.1 Network (3.2 Operations research) 4.1 Geography / geometry 4.2 Space-time Quick summary Key terms table (English-Chinese 中英对照术语表)

Database References Paradigm Experience Partnership Bio Arts

ABOUT MODELS & DATABASE Instant Notes

When we look deep into nature, creatures, and ourselves, many interesting patterns and rules will be found, like feedback, fractal, symmetry, network, hierarchy, wave, randomness, and self-organization, which are composed together as the miracle of lives on the earth. The common language of mathematics, including equations, numbers, matrix, and graphs, is naturally connected with nature. Ecological modeling offers a way of following the nature to understand and sustain the human-nature relationship as a kind of methodology of self-observation and -adaptive system. Inspired by nature, the authors categorize the four main types of ecological modeling as a framework to help more people to be aware of the connection and the utilization. In 2017, the original lectures were presented in China, Japan, and Netherlands and was received the broad interests. The slides were adapted from the lectures.

To categorize

Why?

To match/use them To name them To ‘forget’ terms —connection, mobility To understand To memorize To develop To adapt

模型与 数据库 精要速览 生态学和环境科学的 数学方法及数据资源

报告人：李周园 RG | G | in | f | t

2017-12-05 / 荷兰-瓦赫宁根，瓦赫宁根大学与研究中心 (资源生态组) 2017-10-18 / 中国-厦门, 中科院城市环境研究所 (陈伟强研究员组) 2017-10-12 / 日本-别府, 立命馆亚太大学 (李燕教授组)

模型与 数据库 精要速览 生态学和环境科学的 数学方法及数据资源

报告人：李周园 RG | G | in | f | t

献给清华大学环境学院刘雪华组（环境学院903/905） 献给张江教授（Jake）

提要

当我们凝视自然、生命和我们自己时，会发现许多有意思的模式和规律，比如

反馈、分形、对称、网络、树状、波动、随机、自组织，这些共同造就了地球 上发生的生命奇迹。当我们共享通用的数学语言时，比如方程、数字、矩阵、 几何图形时，我们知道数学与自然有着天然的联系。生态模型以一种自省和自 我调节的系统方法出现，为我们理解和维护和谐的人与环境关系指出一条明路。

模型与数据库 精要速览

受大自然的启示，作者将生态模型划归为四个大的类别，以此帮助更多人认识 到这些方法之间的联系以及对应用途。2017年，作者在中国、日本和荷兰曾就 报告内容发表过演讲，反响广泛。本报告在原讲义基础上改编完成。

为什么 要归类?

更准确地匹配、利用模型 更恰切地命名方法、促进交流 （暂时）忘记术语、打破学科壁垒、加强学脉联系 更全面地帮助理解 更系统地帮助记忆 更好地融合发展和创新

中英对照术语表 | p 31

更快适应新的技术和方法挑战

MODELS

4

Categorized into the main types concepts & applications

1.1 Dynamic 1.2 Complexity curves/time

differential equation, calculus

4.1 Geography/Geometry 2.1 Statistic 3.1 Network 2.2 Machine Learning (3.2 Operations research) 4.2 Space-time dots/randomness

lines/relationship

space/pattern

probability theory and statistics

linear algebra, matrix

GIS, observation, imagination, multidimensional thinking

© Zhouyuan Li et al. 2018

10

parameter (r)

1.1 Dynamic (1) concepts kinetics

stock (Nt0)

flow (dN/dt)

stock (Nt1)

structure  | behavior 

dN/dt=r

dN/dt=r N

dN/dt= rmaxN(K-N)/K

dN/dt=rmaxN sin(ωt+φ)

N time (t) (a) Linear

(b) Exponential (+)

(c) Goal-seeking (-)

© Zhouyuan Li et al. 2018

(d) Waving

11

Population, economy, material flow analysis, system growth simulation and prediction, etc.

1.1 Dynamic (2) applications EXAMPLE

• Club de Rome, and Donella H. Meadows. The limits to growth: a report for the Club of Rome's Project on the predicament of mankind. New York: Universe Books, 1972. • Ford, Frederick Andrew. Modeling the environment: an introduction to system dynamics models of environmental systems. Island Press, 1999. • Jorgensen, S.E. and Fath, B.D., Fundamentals of ecological modelling: applications in environmental mangement and research. Elsevier. 2011. • Jørgensen, Sven Erik, Brian D. Fath, Søren Nors Nielsen, Federico M. Pulselli, Daniel A. Fiscus, and Simone Bastianoni. Flourishing Within Limits to Growth: Following Nature's Way. Routledge, 2015.

Keilhacker, M. L., & Minner, S. (2017). Supply chain risk management for critical commodities: A system dynamics model for the case of the rare earth elements. Resources, Conservation and Recycling, 125, 349-362.

© Zhouyuan Li et al. 2018

12

0.2 0.15

r =1.2

0.1 0.05

1.2 Complexity (1) concepts

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

N

Iteration

0 1

10

100

1000

0.8

0.2 1

10

1

r = 3.7

0.2 1000

100

1000

Iteration

0 100

10

dN/dt=rN(K-N)/K

Nt0=0.03

0.4

Iteration

0

1000

Iteration

0

over-shoot

0.6

Nt0=0.02

0.4

100

N

0.8

r = 3.7

0.6

0.2

accelerating 1

N

10

r =3.0

0.4

Iteration 1

N

0.6

r =2.4

logistic growth 1

0.8

N

1

10

100

1000

chaos

features  | examples   a) Dynamic system / self-similarity / self adaptive / feedback / coupled b) Scale matters c) ‘Parameter as a king’ d) Initial values sensitive e) From periodic to aperiodic behavior Fractal (Barnsley fern) © Zhouyuan Li et al. 2018

13

1.2 Complexity (1) applications MOVIES

• 郑维敏Weimin Zheng. 正反馈 Postive Feedback. 清华大学出版社 Tsinghua University Press, 1998. • Feldman, David P. "Chaos and fractals: Chaos and Fractals: An Elementary Introduction" Oxford University Press 2012. • Williams, Garnett P. Chaos Theory Tamed. 1997. • Kaye, Brian Howard. Chaos and complexity : discovering the surprising patterns of science and technology. VCH, 1993. • Stewart, Ian Nicholas. "Does God play dice? : the new mathematics of chaos." Iop Publishing 04(2001). • Lance H. Gunderson, C. S. Holling. "Panarchy." Island Press, 2002.

NOVA (TV Series) (2008) Fractal Hunting the Hidden Dimension. Directed by Bill Jersey & Michael Schwarz BBC (Documentary) (2010) The Secret Life of Chaos. Directed by Nic Stacey

© Zhouyuan Li et al. 2018

14

Systematics

1.2 Complexity (1) applications

0.8

N

0.6

1D

0.4 0.2

EXAMPLES

Iteration

0 1

10

100 50 45

Z. Yan., and J. Li. "Giant panda survival crisis remains serious based on the ecosystem catastrophe model." Ecological Modelling 359(2017):128-134. Sugihara, G, et al. "Detecting causality in complex ecosystems." Science 338.6106(2012):496. Staal, A., Dekker, S.C., Xu, C. and van Nes, E.H., 2016. Bistability, spatial interaction, and the distribution of tropical forests and savannas. Ecosystems, 19(6), pp.1080-1091.

MOVIES

Informatic

Cybernetics

1000

Z

Inspiration sources for interdisciplinary

40 35

2D

30

25 20 15 10 5

X

0

-20.00 -15.00 -10.00 -5.00

0.00

5.00

10.00

15.00

20.00

25.00

3D Dissipative system

Catastrophe & Emergence Synergetic

The Butterfly Effects trilogy 2004/2006/2009

© Zhouyuan Li et al. 2018

15

D1

2.1 Statistics (1) concepts

PC2 decrease information entropy

Dn D2

EXAMPLES Ke, C., Li, Z., Liang, Y., Tao, W. and Du, M., 2013. Impacts of chloride de-icing salt on bulk soils, fungi, and bacterial populations surrounding the plant rhizosphere. Applied Soil Ecology, 72, pp.69-78. Liu, X., Liu, X., Skidmore, A. and Garcia, C., 2017. Recovery of woody plant species richness in secondary forests in China: a meta-analysis. Scientific Reports, 7(1), p.10614.

decrease dimensionality

thermodynamics

PC1

X ~ N (μ, σ2) extract the features

procedure  | types  Descriptive statistics ‘5M’: mean, median, mode, maximum, minimum Parameter estimation

Analytical statistics Difference: test (T-, ANOVA…) Similarity: correlation and regression analysis Multi-variable: clustering and ranking

© Zhouyuan Li et al. 2018

16

2.1 Statistics (2) applications

classification regression clustering ranking detection supervised and unsupervised learning

Feature recognition Dimensionality reduction Induction and prediction Semantic analysis

decision trees ensembles neural networks Bayes factor analysis / PCA feature engineering parsing

partial/global/existing/create new

Data mining

Volume-scale Velocity-real-time and streaming Variety-structured and unstructured

 Big data

Variability-diversity in dynamic Veracity-validatable accuracy Value-informative

Machine learning

 

Big data=overall samples (holistic)

© Zhouyuan Li et al. 2018

17

2.2 Machine Learning (1) concepts

Artificial intelligence Machine learning Artificial neural network + backpropagation

Deep learning

© Zhouyuan Li et al. 2018

≈

Advanced multivariable statistics +Signal processing • digital technology rooted from complexity theories • phenomenological methodology and solution to deal with the big data • upgraded knowledge automatic system

18

kernel

2.2 Machine Learning (1) concepts Deep learning (a) Convolutional Neural Network CNN Increasing dimensionality Backpropagation algorithm

Convolution

Partial feature (highlights)

Convolution Pooling Convolution Pooling

Convolutional feature layers

Convolutional feature layers

© Zhouyuan Li et al. 2018

Pooling

Output Fully Connected Layers

Global feature (big picture)

Project

Convolution Pooling Convolution

Convolutional feature layers

Convolutional feature layers

19

2.2 Machine Learning (1) concepts Deep learning (b) Recurrent Neural Network RNN Long short-term memory LSTM

Xt

input gate

ht

Yt

sum

𝐶ሚ | new MC sum

forget gate

input layer

output layer

INPUT sigmoid

C | memory cell (MC)

OUTPUT output gate

hidden layer

© Zhouyuan Li et al. 2018

20

2.2 Machine Learning (2) applications EXAMPLES Guo, S., Xiao, D., Yuan, X., 2017. A Short-Term Rainfall Prediction Method Based on Neural Networks and Model Ensemble. Advances in Meteorological Science and Technology, 01. 郭尚瓒, 肖达, 袁行远, 2017. 基于神经网络 和模型集成的短时降雨预测方法. 气象科 技进展 01. LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521(7553), 436-444. Silver, D., et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. Zhuge, Q., Xu, L., & Zhang, G. (2017) LSTM Neural Network with Emotional Analysis for Prediction of Stock Price. Engineering Letters, 25(2), 167-175. Da Xiao, Ruoyu Liao, Xingyuan Yuan. Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction. ICLR 2018 Hossain, M., et al. (2015) Forecasting the weather of Nevada: A deep learning approach. In Neural Networks, International Joint Conference: 1-6. IEEE.

translation, writing, journalism, education, natural language processing

gaming, financial

game

Ae文

art

LingoCloud

computer vision, remote sensing

ColorfulClouds

medical image diagnose

self-driving

© Zhouyuan Li et al. 2018

21

Ecological Network Analysis

3. Network (1) concepts

Structural: digraph to Adjacency Matrix • In-degree, measures the food sources • Out-degree, measures the predators • Higher Order (Indirect) Pathways • Structural cycling: Eigenvalue, λ, is used to determine cyclicity and rate of path proliferation. Functional: Pathway analysis • Flow / Storage / Utility Analysis

Flow Network

Social Network Analysis

𝜂

• Allometric scaling law: 𝐶𝑖 ∝ 𝑇𝑖 • T=Throughflow, C=impact, • Efficiency & centralization

Distribution • Bridge / distance / tie strength • Centrality / density / structural holes Connection • Assortativity (homophily) / multiplexity • Mutuality / closedness / propinquity Segmentation • clique / social circle • Clustering coefficient (coherence) / cohesion

η=1

η=2

Operations research © Zhouyuan Li et al. 2018

22

3. Network (2) applications

zoonosis / epidemics / endemic diseases

food web

EXAMPLES Garrett, K.A., et al., 2011. Complexity in climate‐change impacts: an analytical framework for effects mediated by plant disease. Plant Pathology, 60(1), pp.15-30. Yang, Y., Wu, L., et al., 2013. Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland. Global Change Biology, 19(2), pp.637-648. Fath, B.D., Scharler, U.M., et al., 2007. Ecological network analysis: network construction. Ecological Modelling, 208(1), pp.49-55. Tian, H., … Xu, B., 2015. Avian influenza H5N1 viral and bird migration networks in Asia. PNAS, 112(1), pp.172-177. Tero, A., et al. (2010) Rules for biologically inspired adaptive network design. Science, 327(5964): 439442. Li, R., Dong, L., Zhang, J., Wang, X., Wang, W.X., Di, Z. and Stanley, H.E., 2017. Simple spatial scaling rules behind complex cities. Nature Communications, 8(1), p.1841. Zhang, J. and Guo, L., 2010. Scaling behaviors of weighted food webs as energy transportation networks. Journal of theoretical biology, 264(3), pp.760-770. Zhang, J. and Wu, L., 2013. Allometry and dissipation of ecological flow networks. PloS One, 8(9), p.e72525. Wang, S., Liu, Y. and Chen, B., 2017. Multiregional input–output and ecological network analyses for regional energy–water nexus within China. Applied Energy. Shi, P., Zhang, J., Yang, B. and Luo, J., 2014. Hierarchicality of trade flow networks reveals complexity of products. PloS One, 9(6), p.e98247. Nowak, M.A., 2006. Evolutionary dynamics. Harvard University Press.

transportation web / self-organization

phytopathology

NETWORK / RELATION microbiology / metabolism

collaboration / citation Financial / world trade web © Zhouyuan Li et al. 2018

urbanization / land use / artificial system

water–energy–food / land-climate-economy nexus 23

4.1Geography/ Geometry (1) concepts (2) applications

• Spatial location, spatial distribution, spatial form, spatial distance, spatial relationship • Geology / geography / geometry; topology / topography • Spatial information classification analysis, buffer analysis, overlay analysis, network analysis, spatial model analysis, spatial statistical analysis, landscape ecology… …

Mapping everything…

© Zhouyuan Li et al. 2018

24

4.2 Space-time (1) concepts

RELATION SPACE material money recyclable things …

information intelligence organization …

TIME energy power human flowing things …

© Zhouyuan Li et al. 2018

Relativeness 3-dimension thinking Scaling 25

4.2 Space-time (1) concepts EXAMPLES School days VS. Vacations Country roads VS. Urban streets

Feels differently ! 3-D thinking

Integrity Energy-material-information nexus (3-in-1) Time-environmental factor-relationship

Growth amount

Bidirectionality Optimization range (y ∝ x2)

Time Growth rate

Growth rate

Environmental factor (space)

Time

Time © Zhouyuan Li et al. 2018

26

Periodicity Fluctuation

4.2 Space-time (1) concepts

W Spr

F

EXAMPLES

Sum

space

Four seasons

time

space © Zhouyuan Li et al. 2018

27

Directionality

4.2 Space-time (1) concepts EXAMPLES Tree growth Community succession Dynasty changed

Panarchy (Evolutionary) Self-adaptive organization Collapse and restoration Resistance and resilience

growth

growth

K periodicity

“Take a historical perspective” (Yinuo Li, 2016, Commencement Speech at Tsinghua campus)

time

Ω

α

O

r irreversible time arrow © Zhouyuan Li et al. 2018

28

Synchronicity correlation VS. causality

4.2 Space-time (2) application

Relativeness Integrity Bidirectionality (partial) Periodicity Directionality (global)

X

…

EXAMPLES

Yt 1

What I discovered and preliminarily explained in my PhD research:

“

t2

t3

…

tn

The variables of system status

of the highly-correlated on spatial dimension is NOT necessarily changed simultaneously by time, as has ‘non-synchronicity’, which is determined by the system structure.” Li, Z., Liu, X., …Ma, T. et al., 2015. Ecological restoration and its effects on a regional climate: the source region of the Yellow River, China. Environmental Science & Technology, 49(10), pp.5897-5904. Li, Z., Wu, W., Liu, X., Fath, B.D., Sun, H., Liu, X., et al., 2017. Land use/cover change and regional climate change in an arid grassland ecosystem of Inner Mongolia, China. Ecological Modelling, 353, pp.86-94.

High spatial correlation  

|

Y

non-synchronicity 

X Y X © Zhouyuan Li et al. 2018

time 29

1.1 Dynamic

1.2 Complexity

2.1 Statistics

2.2 Machine Learning

long-term

holistic

from trajectory to out-of-control

from randomness to predictability

D

3.1 Network (3.2 Operations Research)

4.1Geography/ Geometry

QUICK SUMMARY

4.2 Space-time

G

C S ML

N

Scale matters: dynamic  complex; statisticsmachine learning; input/outputnetworknexus; geometrygeographyspace-time…

© Zhouyuan Li et al. 2018

30

adjacent matrix allometric analytical statistics ANOVA aperiodic artificial intelligence artificial neural network assortativity

邻接矩阵 异速生长 分析性统计 方差分析 非周期 人工智能 人工神经网络 可归纳性

backpropagation behavior bidirectionality big data bridge Butterfly Effects

反向传播 行为 双向性 大数据 桥链接 蝴蝶效应

B

C

calculus Catastrophe causality centrality centralization chaos clique closedness clustering clustering coefficient coherence cohesion collapse complexity connection convolution Convolutional Neural Network (CNN) correlation correlation analysis coupled Cybernetics cyclicity

微积分 灾变 因果性 中心性 集中化 混沌 小集团 封闭性 聚类 聚类系数 粘性, 连贯性 凝聚性 崩溃 复杂性 链接 卷积 卷积神经网络 相关性 相关性分析 耦合 控制论 循环性

data mining deep learning density descriptive statistics differential equation dimensionality directionality Dissipative system distance distribution diversity dynamic dynamical system ecological network analysis efficiency eigen value Emergence energy-material-information nexus exponential

数据挖掘 深度学习 密度 描述性统计 微分方程 维度 方向性 耗散系统 距离 分布 多样性 动态, 动力学 动态系统 生态网络分析 效率 特征值 涌现 能量—物质—信息耦合 指数

feature feedback flow analysis flow network flow variable forget gate fractal fully connected layer

特征 反馈 流量分析 流网络 流量 遗忘门 分形 全连接层

geography geometry goal-seeking hidden layer High spatial correlation, non-synchronicity higher-order pathway analysis high-volume data holistic homophily

地图学 几何学 趋稳 隐含层 空间强相关，时间不同步 高阶路径分析 高密度数据 整体论 同质性

F

G/H

KEY TERMS TABLE

S

I/K/L

D/ E

A

in-degree Informatic information entropy informative initial value input layer integrity interaction interdisciplinary K Strategy kinetics linear linear algebra long short-term memory

输入度 信息论 信息熵 信息丰富的 初始值 输入层 同一性, 整体性 互相作用 交叉科学 K策略 动力学 线性 线性代数 长短期记忆算法

machine learning matrix memory cell multi-dimension space multiplexity multi-variable mutuality network operations research out-degree output layer panarchy parameter parameter estimation periodic periodicity phenomenological pooling probability theory and statistics propinquity

机器学习 矩阵 记忆细胞 多维空间 多重性 多元 交互性 网络 运筹学 输出度 输出层 自适应系统, 扰沌 参量 参数估计 周期 周期性 唯象的 池化 概率论与数理统计 亲缘性

r Strategy ranking real-time Recurrent Neural Network (RNN) regression analysis relativeness resilience resistance restoration

r策略 排序 即时 递归神经网络 回归分析 相对性 弹性 抗性 恢复

M/N/O/P

R

scale scaling scaling law segmentation self adaptive system self-similarity sigmoid function signal processing social circle social network analysis space-time statistic stock variable storage analysis streaming data structural holes structure structured data synchronicity Synergetic system dynamic Systematics

尺度 标度变化 标度律 划分 自适应系统 自相似 神经元突变函数 信号处理 社交圈 社会网络分析 时空 统计学 存量 存量分析 流变数据 结构空位 结构 结构化数据 同步性 协同论 系统动力学 系统论

thermodynamics throughflow tie strength topography topology t-test unstructured data utility analysis validatable accuracy variability variety velocity veracity waving

热力学 流经量 关系强度 地形 拓扑学 t-检验 非结构化数据 利用率分析 可验证的精确性 变化性 多种类 速度 准确性 波动

T/U/V

© Zhouyuan Li et al. 2018

BACK 31

DATABASE: INFORMATION RESOURCE Organization

Title

(Online publishing time)

ESA

Global CCI LUCC

NASA

Global MODIS-ET （2017.1）

NASA

Global NASA Earth Observation

NOAA

Global Nighttime light（2012）

EU

Global population density mapping

Tsinghua ESS NOAA

（2017.4）

Link

http://maps.elie.ucl.ac.be/CCI/viewer/ http://files.ntsg.umt.edu/data/ET_global_monthly/ https://neo.sci.gsfc.nasa.gov/analysis/index.php https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html （2014）

Global LUCC Simulation Mapping（2014）

http://luminocity3d.org/WorldPopDen/#3/12.00/10.00 http://data.ess.tsinghua.edu.cn/data/Simulation/

Global Forecast System (GFS) (2010s ) https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs

EU, ESA

Copernicus marine environment monitoring service (2016/2017) http://marine.copernicus.eu/

CAS

Geospatial data Cloud

CAS

Global Land Surface Water Dataset at 30m Resolution（2014）

CAS

Global Artificial Land Surface Dataset at 30 m Resolution-2010 （2014） http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=163

NASA

MODIS Datasets

(2013)

http://www.gscloud.cn/ http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=159

(2000s) https://modis.gsfc.nasa.gov/data/

NASA-USGS Landsat Datasets (2000s)

https://landsat.usgs.gov/ ; https://earthexplorer.usgs.gov/

© Zhouyuan Li et al. 2018

Swarma AI Campus. PyTorch Project PyTorch Code and Resource List. goo.gl/oMiEXg (2017)

32 32

REFERENCES Club de Rome, and Donella H. Meadows. 1972. The limits to growth: a report for the Club of Rome's Project on the predicament of mankind. New York: Universe Books. Ford, Frederick Andrew. 1999. Modeling the environment: an introduction to system dynamics models of environmental systems. Island Press. Jorgensen, S.E. and Fath, B.D., 2011. Fundamentals of ecological modelling: applications in environmental mangement and research. Elsevier. Jørgensen, Sven Erik, Brian D. Fath, Søren Nors Nielsen, et al. 2015. Flourishing Within Limits to Growth: Following Nature's Way. Routledge. Weimin Zheng. 1998. Postive Feedback. Tsinghua University Press. Keilhacker, M.L. and Minner, S., 2017. Supply chain risk management for critical commodities: A system dynamics model for the case of the rare earth elements. Resources, Conservation and Recycling, 125, pp.349-362. Feldman, D.P., 2012. Chaos and fractals: an elementary introduction. Oxford University Press. Williams, G.P., 1997. Chaos theory tamed. Joseph Henry Press. Kaye, B.H., 1993. Chaos&Complexity: discovering the surprising patterns of science and technology. Stewart, I., 1997. Does God play dice?: The new mathematics of chaos. Penguin UK. Lance H. Gunderson, C. S. Holling. , 2002. Panarchy: understanding transformations in human and natural systems. Island press. Staal, A., Dekker, S.C., Xu, C. and van Nes, E.H., 2016. Bistability, spatial interaction, and the distribution of tropical forests and savannas. Ecosystems, 19(6), pp.1080-1091. Yan, Z. and Li, J., 2017. Giant panda survival crisis remains serious based on the ecosystem catastrophe model. Ecological Modelling, 359, pp.128-134. Sugihara, G., May, R., Ye, H., Hsieh, C.H., Deyle, E., Fogarty, M. and Munch, S., 2012. Detecting causality in complex ecosystems. science, p.1227079. Ke, C., Li, Z., Liang, Y., Tao, W. and Du, M., 2013. Impacts of chloride de-icing salt on bulk soils, fungi, and bacterial populations surrounding the plant rhizosphere. Applied soil ecology, 72, pp.69-78. Liu, X., Liu, X., Skidmore, A. and Garcia, C., 2017. Recovery of woody plant species richness in secondary forests in China: a meta-analysis. Scientific reports, 7(1), p.10614. Maqsood, I., Khan, M.R. and Abraham, A., 2004. An ensemble of neural networks for weather forecasting. Neural Computing & Applications, 13(2), pp.112-122. Guo, S., Xiao, D., Yuan, X., 2017. A Short-Term Rainfall Prediction Method Based on Neural Networks and Model Ensemble. Advances in Meteorological Science and Technology, 01. Hossain, M., Rekabdar, B., Louis, S.J. and Dascalu, S., 2015, July. Forecasting the weather of Nevada: A deep learning approach. In Neural Networks (IJCNN), 2015 International Joint Conference on (pp. 1-6). IEEE. LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. Nature, 521(7553), 436-444. Silver, D., et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. Zhuge, Q., Xu, L., & Zhang, G. 2017. LSTM Neural Network with Emotional Analysis for Prediction of Stock Price. Engineering Letters, 25(2), 167-175. Da Xiao, Ruoyu Liao, Xingyuan Yuan. Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction. ICLR 2018 Garrett, K.A., et al., 2011. Complexity in climate‐change impacts: an analytical framework for effects mediated by plant disease. Plant Pathology, 60(1), pp.15-30. Yang, Y., Wu, L., et al., 2013. Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland. Global Change Biology, 19(2), pp.637-648. Fath, B.D., Scharler, U.M., et al., 2007. Ecological network analysis: network construction. Ecological Modelling, 208(1), pp.49-55. Tian, H., … Xu, B., 2015. Avian influenza H5N1 viral and bird migration networks in Asia. PNAS, 112(1), pp.172-177. Tero, A., et al. (2010) Rules for biologically inspired adaptive network design. Science, 327(5964): 439-442. Li, R., Dong, L., Zhang, J., Wang, X., Wang, W.X., Di, Z. and Stanley, H.E., 2017. Simple spatial scaling rules behind complex cities. Nature Communications, 8(1), p.1841. Zhang, J. and Guo, L., 2010. Scaling behaviors of weighted food webs as energy transportation networks. Journal of theoretical biology, 264(3), pp.760-770. Zhang, J. and Wu, L., 2013. Allometry and dissipation of ecological flow networks. PloS One, 8(9), p.e72525. Wang, S., Liu, Y. and Chen, B., 2017. Multiregional input–output and ecological network analyses for regional energy–water nexus within China. Applied Energy. Shi, P., Zhang, J., Yang, B. and Luo, J., 2014. Hierarchicality of trade flow networks reveals complexity of products. PloS One, 9(6), p.e98247. Nowak, M.A., 2006. Evolutionary dynamics. Harvard University Press. Li, Z., Liu, X., …Ma, T. et al., 2015. Ecological restoration and its effects on a regional climate: the source region of the Yellow River, China. Environmental Science & Technology, 49(10), pp.5897-5904. Li, Z., Wu, W., Liu, X., Fath, B.D., Sun, H., Liu, X., et al., 2017. Land use/cover change and regional climate change in an arid grassland ecosystem of Inner Mongolia, China. Ecological Modelling, 353, pp.86-94. Li, Z. 2017. The Relationship between Land Use Cover Change and the Underlying Surface Climate in the Grassland Ecosystems of China. (PhD Dissertation). Tsinghua University. Li, Z., Fath, B. D., 2016. Ecological Network Analysis: Quick Intro (presentation). DOI: 10.13140/RG.2.2.21509.45280 Li. Z., et al. 2017. A Holistic and Intuitive Way: Complexity Science and Remote Sensing for Ecosystems Studies(presentation). DOI: 10.13140/RG.2.2.31236.73605

© Zhouyuan Li et al. 2018

33

INDUCTION

DEDUCTION algebraic relation

logic

mathematic language

kinetic process (dynamic)

geometric relation

PARADIGM

Rationalism

physical landscape

physical phenomena

thermodynamics (statistics) pattern chemical phenomena

social phenomena

biological phenomena

Empiricism

finding physical landscape

哲学范式 发现物理图景

© Zhouyuan Li et al. 2018

34

“Simple model, complex system”. The modeling taught us how to simplify the complex outside world in the mind. The simplest natural laws ruled in every single step. After accumulating through space and time, their effect will be amplified to be huge, unpredictably emerged, and out of the so-called ‘control’. Such experience enlightened me to find the certainty in the randomness, the possible fluctuation in the trajectory, and the infinity within the limitation. These paradoxes made me to rethink the big picture of on the world and looked up to the miracle of life, knowing every lives are equal, and getting along well with myself in a natural and peaceful way.

EXPERIENCE

“简单模型，复杂系统”。建模告诉我们如何用心简化一个外在的复杂世界。每一步 都是最为简单自然的法则在支配，通过时间和空间的累积，其作用将被放大、涌现， 直至“失控“。这种体验启示我发现无常中的确定、既定轨道中的波动，以及有限中 的无限。这些辩证统一的矛盾，令我反思世界的全貌，对生命奇迹常怀敬意，理解 众生平等，并能自然平和地与自己相处。

“ 建模心得

Simple model,

complex system”

“简单模型，复杂系统” © Zhouyuan Li et al. 2018

35

PARTNERSHIP

Contributors

伙伴关系 报告贡献者

© Zhouyuan Li et al. 2018

36

BIO

Jul-2012, BSc of Environmental Science, Beijing Forestry University

Jan-2017, PhD, School of Environment, Tsinghua University (Ecology and Remote Sensing) Apr-July-2017, WIMEK Research Fellow at Wageningen University, Netherlands

‘Joey’ Zhouyuan Li, PhD. Sep-2017, Guest Researcher at ColorfulClouds AI Tech Ltd., Co., Advisory Board Member of ISEM.

关于作者 李周园 (Joey)

2012年7月，本科毕业于北京林业大学环境科学专业 2017年1月，博士毕业于清华大学环境学院，生态学与遥感技术方向 2017年4—7月，在荷兰Wageningen大学WIMEK访问学者 2017年9月至今，在彩云科技公司任客座研究员，国际生态模型学会（ISEM）顾问委员

© Zhouyuan Li et al. 2018

37

ARTS

Brug in de regen, 1887 (inspired and copied from the Japanese Ukiyo-e, Sudden Shower over Shin-Ōhashi bridge and Atake, 1857)

Canal with Women Washing, 1888

Starry Night Over the Rhone, 1888

Bulb Fields, 1883

Alychamps, 1888

By Vincent Willem van Gogh, a world-known Dutch Post-Impressionist painter.

Thanks for your attention and sharing RG | G | in | f | t

© Zhouyuan Li et al. 2018

39