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

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

Jan 25, 2023 118 likes •767 views

Ecological Modeling and Decision Support Systems P. Struss and O. Dressler WS 14/15 WS 14/15 EMDS 1 - 1 Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich Ecological Modeling and Decision Support

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

  • P. Struss

and O. Dressler WS 14/15

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1 - 1

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

Ecological Modeling and Decision Support Systems

WS 14/15 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

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

„Eco-Informatics“?

WS 14/15 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?

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1

Example: Impact of Introduction of Trout in New Zealand

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

Example: Impact of Introduction of Trout in New Zealand

WS 14/15 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

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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 14/15 EMDS 1 - 6

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

 Difference – hiding/visibility – Daytime - night

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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 14/15 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

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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 14/15 EMDS 1 - 8

1 2 3 4

N G T Invertebrate biomass (g m2)

1 2

N G T Algal biomass (µg cm2)

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

Biomass – Production and Demand

WS 14/15 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

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

Further Impact?

WS 14/15 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, …

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1

Ecology: Definitions and Basic Concepts

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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 14/15 EMDS 1 - 12

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

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

Levels of Interaction – Individual

WS 14/15 EMDS 1 - 13

Individual

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

Levels of Interaction – Population

WS 14/15 EMDS 1 - 14

Individual Population

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

Levels of Interaction – Community

WS 14/15 EMDS 1 - 15

Individual Population Community

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

Levels of Interaction – Ecosystem

WS 14/15 EMDS 1 - 16

Individual Population Community Ecosystem

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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 14/15 EMDS 1 - 17

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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 14/15 EMDS 1 - 18

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

Different Sciences and Knowledge Sources

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

WS 14/15 EMDS 1 - 19

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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 14/15 EMDS 1 - 20

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1 - 21

Ecology: Tasks and Goals

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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 14/15 EMDS 1 - 22

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

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

Understand in Order to Influence

WS 14/15 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

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1 - 24

Example: Degradation of Mangroves in India

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

Optimism - „We will preserve local flora and fauna“

WS 14/15 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.“

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

Meanwhile, Upstream ...

WS 14/15 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

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

“Side-effects” ...

WS 14/15 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”

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1

Humans and “Environment”

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

“Environment”??...

WS 14/15 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

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

Anthropocentric Perspective

WS 14/15 EMDS 1 - 30

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

“Side-effects” ...

WS 14/15 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

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

The World, Including Us

WS 14/15 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

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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 14/15 EMDS 1 - 33

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1 - 34

Exercise

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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 14/15 EMDS 1 - 35

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1

Challenges for IT

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

How Can IT Help?

WS 14/15 EMDS 1 - 37

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

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

How Can IT Help? - 1

WS 14/15 EMDS 1 - 38

Describe Explain Predict Manage, Control Observe Plan Obs.

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

IT Support: Collecting Data

WS 14/15 EMDS 1 - 39

  • (Remote) sensing
  • E.g. satelite data
  • Importance of spatial aspects
  • Problem: volume of spatial data
  • Possible problem: covering long-time ranges
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Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

IT Support: Storing and Retrieving Data

WS 14/15 EMDS 1 - 40

  • Data base technology
  • Challenge: spatial representation
  •  Geographical information systems (GIS)
  • Problem: Integration of different DBs
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Model-Based Systems & Qualitative Reasoning Group of the Technical University of Munich

How Can IT Help? - 2

WS 14/15 EMDS 1 - 41

Describe Explain Predict Manage, Control Observe Plan Obs.

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

IT Support: Analyzing and Interpreting Data

WS 14/15 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

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

How Can IT Help? - 3

WS 14/15 EMDS 1 - 43

Describe Explain Predict Manage, Control Observe Plan Obs.

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

IT Support: Prediction

WS 14/15 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

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

How Can IT Help? - 4

WS 14/15 EMDS 1 - 45

Describe Explain Predict Manage, Control Observe Plan Obs.

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

IT Support: Drawing Conclusions and Taking Decisions

WS 14/15 EMDS 1 - 46

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

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

Current Support through IT

WS 14/15 EMDS 1 - 47

Describe Explain Predict Manage, Control Observe Plan Obs.

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

problem solving

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

WS 14/15 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

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

The Challenge for Knowledge Representation and Reasoning

WS 14/15 EMDS 1 - 49

Environmental/Ecological System

Conceptual Model

Selection Interpretation Modeling Problem Solving

Data Processing

Model-based Systems Acting

Analysis

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

Current Support through IT

WS 14/15 EMDS 1 - 50

Describe Explain Predict Manage, Control Observe Plan Obs.

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

problem solving

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

Focus of the Lecture: the Weak Parts

WS 14/15 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
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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 14/15 EMDS 1 - 52

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1 - 53

Example: Decision Support for Drinking Water Treatment

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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 14/15 EMDS 1 - 54

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

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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 14/15 EMDS 1 - 55

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

Iron Iron

transport

Iron

ascending redissolving

Iron

pH = -

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

Goal: Find Potential Remedies Automatically

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

Iron Iron

transport

Iron

ascending redissolving

Iron

pH = - Oxidation

OxidationAgent

! Reduce iron

concentration

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

Ecological Modeling and Decision Support Systems

WS 14/15 EMDS 1

Benefits Expected from Use of IT

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

Supporting Experts

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

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

Supporting Non-Experts

WS 14/15 EMDS 1 - 59

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

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

The Vision: Research and Decision Making

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Decision Making Research

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

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

Ecological Modeling and Decision Support Systems

WS 14/15 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

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

Outline

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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

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

WS 14/15 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: Mehdi, Gulnar <gulnar.mehdi@tum.de>
  • Exams?
  • Two periods: Oct. 20 – Nov.5, Jan. 14 – Jan 28

Questions?

?

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