Ecological Modeling and Decision Support Systems P. Struss and O. - - PowerPoint PPT Presentation

▶
ecological modeling and decision support systems
SMART_READER_LITE
LIVE PREVIEW

Ecological Modeling and Decision Support Systems P. Struss and O. - - PowerPoint PPT Presentation

Jul 19, 2023 112 likes •748 views

Ecological Modeling and Decision Support Systems P. Struss and O. Dressler WS 13/14 WS 13/14 EMDS 1 - 1 Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich Ecological Modeling and Decision Support

slide-1
SLIDE 1

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

  • P. Struss

and O. Dressler WS 13/14

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 1

slide-2
SLIDE 2

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 2

1 The Topic

 Definition of ecology  Concepts in ecology  Environmental problems  The role of IT  The special challenges for IT  Decision support  The focus of the course

slide-3
SLIDE 3

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

„Eco-Informatics“?

WS 13/14 EMDS 1 - 3

 Simply an application of computer science to a particular domain?  Like bio-informatics, medicine informatics, …  Same methods and techniques  E.g. DB technology, simulation, image analysis, …  Specific challenges for IT in ecology?   What is ecology?   What could be supported by IT?

slide-4
SLIDE 4

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1

Example: Impact of Introduction of Trout in New Zealand

  • 4
slide-5
SLIDE 5

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Example: Impact of Introduction of Trout in New Zealand

WS 13/14 EMDS 1 - 5

 Trout introduced to NZ rivers (1867)  For fishing  Compete with native fish (Galaxias)  Both feed on invertebrates  (sections of) rivers – No fish – Trout only – Galaxia only – Both species  So what?  Impact?  Field study

slide-6
SLIDE 6

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Counting Visible Invertebrates

 Different ways of locating prey – Trout: visually – Galaxias: mechanically

WS 13/14 EMDS 1 - 6

 Difference – hiding/visibility – Daytime - night

Nesameletus visible

4 8 12 12 16 16

Galaxias stream Trout stream

Day Day Nigh ght

Deleatidium visible

4 8 12 12

No fish Galaxias Trout

slide-7
SLIDE 7

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Abundance of the Fish Species

 Trout migrate upstream  Prevented by waterfalls   correlation with elevation

WS 13/14 EMDS 1 - 7

9 54 64 71 481 (53) 339 (31) 567 (29) 324 (28) Trout + Galaxias No fish Galaxias

  • nly

Brown trout

  • nly

0.0 (0) 4.37 (0.64) 12.3 (2.05) 0.42 (0.05) NUMBER OF WATERFALLS DOWNSTREAM ELEVATION (m ABOVE SEA LEVEL) VARIABLES 46.7 (8.5) 15.8 (2.3) 22.1 (2.8) 18.9 (2.1) % OF THE BED COMPOSED OF PEBBLES NUMBER OF SITES SITE TYPE

slide-8
SLIDE 8

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Impact on Invertebrates and Algae

 Compared to Galaxias  Trout: – reduced population of invertebrates – Increased biomass of algae

WS 13/14 EMDS 1 - 8

1 2 3 4

N G T Invertebrate biomass (g m2)

1 2

N G T Algal biomass (µg cm2)

slide-9
SLIDE 9

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Biomass – Production and Demand

WS 13/14 EMDS 1 - 9 Production Demand

Production/demand (g AFDM-1 m-2) Invertebrates

2 4 6 8 10 12 14

Trout Galaxias Galaxias Trout

0,5 1 1,5 2 2,5

Fish Algae

50 100 150 200 250 300 350

Trout Galaxias

AFDM: Ash-free Dry Mass

slide-10
SLIDE 10

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Further Impact?

WS 13/14 EMDS 1 - 10

 Potential continuation of the causal chain  Algae feed on nitrate, ammonium, sulfate   Reduced concentration of nitrate, ammonium, sulfate downstream   … …  Boundaries of the analysis, the model, …

slide-11
SLIDE 11

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1

Ecology: Definitions and Basic Concepts

  • 11
slide-12
SLIDE 12

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecology – (One) Definition

“The scientific study of the distribution and abundance of organisms and the interactions that determine distribution and abundance” (Townsend et al. 08)

WS 13/14 EMDS 1 - 12

Interactions?  at various levels – Individuals – Species – Physical environment

slide-13
SLIDE 13

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Levels of Interaction – Individual

WS 13/14 EMDS 1 - 13

Individual

slide-14
SLIDE 14

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Levels of Interaction – Population

WS 13/14 EMDS 1 - 14

Individual Population

slide-15
SLIDE 15

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Levels of Interaction – Community

WS 13/14 EMDS 1 - 15

Individual Population Community

slide-16
SLIDE 16

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Levels of Interaction – Ecosystem

WS 13/14 EMDS 1 - 16

Individual Population Community Ecosystem

slide-17
SLIDE 17

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Different Spatial Scales

 Global climate change  ocean currents  fish populations  …  Plant population in a rain forest  …  Inhabitants of water-filled tree holes  …  Bacteria in termites’ guts

WS 13/14 EMDS 1 - 17

slide-18
SLIDE 18

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Different Temporal Scales

 Ecological succession since the Ice Age  …  Migration and mating cycle of turtles  …  Organisms in decomposition of sheep dung

WS 13/14 EMDS 1 - 18

slide-19
SLIDE 19

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Different Sciences and Knowledge Sources

 Biology  Chemistry  Physics  Geophysics  Hydrology  …

WS 13/14 EMDS 1 - 19

slide-20
SLIDE 20

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

For Instance, Trout Field Study – Aspects

 Levels to be considered?  Spatial aspects?  Temporal aspects?  Disciplines involved?

WS 13/14 EMDS 1 - 20

slide-21
SLIDE 21

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 21

Ecology: Tasks and Goals

slide-22
SLIDE 22

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecology – Goals?

“The scientific study of the distribution and abundance of organisms and the interactions that determine distribution and abundance” (Townsend et al. 08)

WS 13/14 EMDS 1 - 22

 Description …  … only?  Understanding …  … only?  Prediction  … only? Describe Explain Predict

slide-23
SLIDE 23

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Understand in Order to Influence

WS 13/14 EMDS 1 - 23

Manage, Control Describe Explain Predict Motivation:  Limit bad impact of human activity  Secure continued exploitation  “Environmental problems”

Ref.: Townsend et al., Essentials of Ecology

slide-24
SLIDE 24

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 24

Example: Degradation of Mangroves in India

slide-25
SLIDE 25

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Optimism - „We will preserve local flora and fauna“

WS 13/14 EMDS 1 - 25

 „In this area the Forest Department of the Pichavaram Mangroves has started management activities in 1995 in order to preserve the local flora and fauna.“

slide-26
SLIDE 26

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Meanwhile, Upstream ...

WS 13/14 EMDS 1 - 26

Dams in Cauvery River Reduction of Sediments in the River Less Deposition in River Delta Trough-shaped Basin Stagnant Water Increased Salinity Degradation of Mangroves Reduced Shelter Against Cyclones, Tsunamis

slide-27
SLIDE 27

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

“Side-effects” ...

WS 13/14 EMDS 1 - 27

Dams in Cauvery River Reduction of Sediments in the River Less Deposition in River Delta Trough-shaped Basin Stagnant Water Increased Salinity Evaporation Degradation

  • f Mangroves

Cyclones

“Environment”

slide-28
SLIDE 28

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1

Humans and “Environment”

  • 28
slide-29
SLIDE 29

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

“Environment”??...

WS 13/14 EMDS 1 - 29

“Environment”

 Limit bad impact of human activity  Secure continued exploitation  “Environmental problems”   Problems of human activity, economy, health, …  Welt  Umwelt!!  Anthropocentric perspective

slide-30
SLIDE 30

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Anthropocentric Perspective

WS 13/14 EMDS 1 - 30

slide-31
SLIDE 31

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

“Side-effects” ...

WS 13/14 EMDS 1 - 31

Dams in Cauvery River Reduction of Sediments in the River Less Deposition in River Delta Trough-shaped Basin Stagnant Water Increased Salinity Evaporation Degradation

  • f Mangroves

Cyclones

slide-32
SLIDE 32

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

The World, Including Us

WS 13/14 EMDS 1 - 32

Understand!  The complex interactions of organisms and natural phenomena and systems  Human activities as additional influences in this network of interactions

Dams in Cauvery River Reduction of Sediment in the River Less Deposition in River Delta Trough-shaped Basin Stagnant Water Increased Salinity Degradation

  • f Mangroves

Cyclones Evaporation

slide-33
SLIDE 33

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Causal Chains – Distant and Paradox Effects

 Building dams  more tsunami victims  Introduce trout  more algae  Extinguish forest fires  more trees and homes destroyed by fire  Extinguish fires in Sequoia forest  Sequoias become extinct  Treat cattle with Diclophenac  more diseases of people and difficult burial of dead Parsis  …

WS 13/14 EMDS 1 - 33

slide-34
SLIDE 34

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 34

Exercise

slide-35
SLIDE 35

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Trout Field Study - Exercise

Design a semi-formal or diagrammatic representation that describes and explains the impact of introduction of trout in NZ  Intuitively  Mainly non-verbal  May combine different forms of representation  There is no unique solution!  There is no “wrong” form of representation!

WS 13/14 EMDS 1 - 35

slide-36
SLIDE 36

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1

Challenges for IT

  • 36
slide-37
SLIDE 37

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help?

WS 13/14 EMDS 1 - 37

Describe Explain Predict Manage, Control Extended view  Basis: observation, data  Planning Experiments/Field Studies Observe Plan Obs.

slide-38
SLIDE 38

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help? - 1

WS 13/14 EMDS 1 - 38

Describe Explain Predict Manage, Control Observe Plan Obs.

slide-39
SLIDE 39

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Collecting Data

WS 13/14 EMDS 1 - 39

  • (Remote) sensing
  • E.g. satelite data
  • Importance of spatial aspects
  • Problem: volume of spatial data
  • Possible problem: covering long-time ranges
slide-40
SLIDE 40

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Storing and Retrieving Data

WS 13/14 EMDS 1 - 40

  • Data base technology
  • Challenge: spatial representation
  •  Geographical information systems (GIS)
  • Problem: Integration of different DBs
slide-41
SLIDE 41

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help? - 2

WS 13/14 EMDS 1 - 41

Describe Explain Predict Manage, Control Observe Plan Obs.

slide-42
SLIDE 42

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Analyzing and Interpreting Data

WS 13/14 EMDS 1

 Statistical analysis  Image processing and analysis  E.g. vegetation coverage from satelite data  Challenges – Huge volume of data – Grasping the meaning of data – Image understanding

  • 42
slide-43
SLIDE 43

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help? - 3

WS 13/14 EMDS 1 - 43

Describe Explain Predict Manage, Control Observe Plan Obs.

slide-44
SLIDE 44

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Prediction

WS 13/14 EMDS 1 - 44

 Numerical models and simulation  Challenges: – Many interactions – Many different aspects ( partial models) – Non-numerical data, information, knowledge – Conceptual modeling – E.g. causality, explanation, causal understanding – Model boundaries – Characterize scope of a model (assumptions) – Support model development – …  Modeling as Knowledge Representation

slide-45
SLIDE 45

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help? - 4

WS 13/14 EMDS 1 - 45

Describe Explain Predict Manage, Control Observe Plan Obs.

slide-46
SLIDE 46

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Drawing Conclusions and Taking Decisions

WS 13/14 EMDS 1 - 46

 Environmental decision support systems  Challenges: – Automated problem solving – Many different aspects – Integrating ecological knowledge with social, economic, political aspects  Automated Reasoning

slide-47
SLIDE 47

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Current Support through IT

WS 13/14 EMDS 1 - 47

Describe Explain Predict Manage, Control Observe Plan Obs.

  • Mainly data processing
  • Numerical simulation
  • Weak for knowledge processing and

problem solving

slide-48
SLIDE 48

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

WS 13/14 EMDS 1 - 48

Data Analysis, Simulation (Numerical) Model DB, GIS Data Data Acquisition Remote Sensing

Analysis Selection Interpretation Modeling Problem Solving

Acting

Conceptual Model

The Role of Information Technology

Environmental/Ecological System

slide-49
SLIDE 49

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

The Challenge for Knowledge Representation and Reasoning

WS 13/14 EMDS 1 - 49

Environmental/Ecological System

Conceptual Model

Selection Interpretation Modeling Problem Solving

Data Processing

Model-based Systems Acting

Analysis

slide-50
SLIDE 50

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Current Support through IT

WS 13/14 EMDS 1 - 50

Describe Explain Predict Manage, Control Observe Plan Obs.

  • Mainly data processing
  • Numerical simulation
  • Weak for knowledge processing and

problem solving

slide-51
SLIDE 51

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Focus of the Lecture: the Weak Parts

WS 13/14 EMDS 1 - 51

Describe Explain Predict Manage, Control Observe Plan Obs.

  • (Conceptual) modeling and prediction
  • Knowledge-based decision support and

automated problem solving

  •  Areas with eco-specific challenges to IT
slide-52
SLIDE 52

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Challenges for IT in Ecology

 Support deeper understanding – Support modeling process – Represent essential concepts – E.g. population, predation, migration, …  Provide common ontology for modeling  (Causal) explanation, education  Automated reasoning  Knowledge-based decision support

WS 13/14 EMDS 1 - 52

slide-53
SLIDE 53

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 53

Example: Decision Support for Drinking Water Treatment

slide-54
SLIDE 54

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

An Example from the Water Treatment Domain

Metalic taste of drinking water  The "metallic taste" is the human perception of iron in the water  Transported by pumping and ascending in the reservoir  Ultimately: dissolved from the sediment  Precondition: acidic conditions

WS 13/14 EMDS 1 - 54

Sediment Hypolimnion Epilimnion Tank Pump Drinking Water Observation: "metallic taste"

slide-55
SLIDE 55

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Goal: Detecting the Causes of Problems Automatically

Metallic taste of drinking water  The "metallic taste" is the human perception of iron in the water  Transported by pumping and ascending in the reservoir  Ultimately: dissolved from the sediment  Precondition: acidic conditions

WS 13/14 EMDS 1 - 55

Sediment Hypolimnion Epilimnion Tank Pump Drinking Water Observation: "metallic taste" perception

Iron Iron

transport

Iron

ascending redissolving

Iron

pH = -

slide-56
SLIDE 56

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Goal: Find Potential Remedies Automatically

WS 13/14 EMDS 1 - 56

Observation: "metallic taste" Sediment Hypolimnion Epilimnion Tank Pump Drinking Water perception

Iron Iron

transport

Iron

ascending redissolving

Iron

pH = - Oxidation

OxidationAgent

! Reduce iron

concentration

slide-57
SLIDE 57

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1

Benefits Expected from Use of IT

  • 57
slide-58
SLIDE 58

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Supporting Experts

WS 13/14 EMDS 1 - 58

 Developing models  Analysis, interpretation of observations  Transfer of results  Exchanging and reusing models Expert Expert Expert

slide-59
SLIDE 59

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Supporting Non-Experts

WS 13/14 EMDS 1 - 59

 Understanding, explanations  Analysis, interpretation of observations  Proposal and assessment of actions Non-Expert Expert

slide-60
SLIDE 60

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

The Vision: Research and Decision Making

WS 13/14 EMDS 1 - 60

Decision Making Research

Biological, chemical, hydrological … models Social, political, economic … models

slide-61
SLIDE 61

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Ecological Modeling and Decision Support Systems

WS 13/14 EMDS 1 - 61

1 The Topic

 Definition of ecology  Concepts in ecology  Environmental problems  The role of IT  The special challenges for IT  Decision support  The focus of the course

slide-62
SLIDE 62

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

Outline

WS 13/14 EMDS 1 - 62

1 The topic 2 Environmental decision-support systems 2.1 Conceptualization 3 Modeling 2 Environmental decision-support systems 2.2 Realization 4 Application issues and challenges

slide-63
SLIDE 63

Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

WS 13/14 EMDS 1 - 63

Organizational Issues

  • 3 credits
  • Informatics
  • Architecture (Advanced Construction and Building Technology)
  • Slides for download (pdf)
  • http://mqm.in.tum.de/teaching/EMDS/Material.php
  • In advance (usually …)
  • Slides not self-contained!
  • Basis for taking notes
  • Script (after presentation)
  • Don’t try exams without attendance!
  • Contact: Umbreen Sabir <umbreensabir@yahoo.com>
  • After October: Mehdi, Gulnar <gulnar.mehdi@tum.de>
  • Exams?
  • Two periods: Oct. 21 – Nov.18, Jan. 13 – Feb. 3

Questions?

?