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Challenges for Machine Learning in Computational Sustainability
Published on Jan 16, 20137481 Views
Research in computational sustainability seeks to develop and apply methods from computer science to the many challenges of managing the earth's ecosystems sustainably. Viewed as a control problem, ec
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Chapter list
Challenges for Machine Learning in Computational Sustainability00:00
Sustainable Management of the Earth’s Ecosystems00:49
Why?01:41
Computer Science can help!02:26
Computational Sustainability02:56
Outline03:41
Example Research Challenges Data Acquisition04:03
Data Interpretation06:03
Data Integration07:25
Model Fitting08:33
State of the Art: STEM Model of Bird Species Distribution10:07
Policy Optimization11:26
State of the Art: Reserve Design from a Species Distribution Model (1)12:24
State of the Art: Reserve Design from a Species Distribution Model (2)12:57
State of the Art: Reserve Design from a Species Distribution Model (3)13:03
Policy Execution13:25
Drill Down: Three Projects at Oregon State15:03
Project eBird www.ebird.org15:52
Species Distribution Modeling from Citizen Science Data17:05
Imperfect Detection18:18
Multiple Visits to the Same Sites19:30
Occupancy-Detection Model20:45
Standard Approach: Log Linear (logistic regression) models22:38
Results on Synthetic Species with Nonlinear Dependencies23:09
A Flexible Predictive (non-Latent) Model24:36
Predictive Model Results25:28
Two Approaches: Summary26:14
The Dream27:32
A Simple Idea: Parameterize 𝐹 and 𝐺 as boosted trees27:46
Results: OD-BRT28:15
Handling Variable Expertise28:31
Expert vs. Novice Differences28:56
Drill Down: Three Projects at Oregon State29:43
BirdCast: Understanding and Forecasting Bird Migration29:48
Modeling Goal: Spatial Hidden Markov Model31:58
Problem: We have only aggregate data32:31
Solution: Collective Graphical Models33:15
Solution: Collective Graphical Models (2)34:20
Inference in Collective Graphical Models35:10
The Migration Model36:03
With Added Covariates36:18
Drill Down: Three Projects at Oregon State36:28
Invasive Species Management in River Networks36:36
Markov Decision Process37:13
Dynamics and Objective38:23
Algorithm DDV (1)39:20
Algorithm DDV (2)40:39
Results on “RiverSwim” benchmark41:12
Published Rule of Thumb Policies for Invasive Species Management41:51
Cost Comparisons: Rule of Thumb Policies vs. DDV42:17
Summary42:26
Distinctive Characteristics of Sustainability Problems43:12
Computational Sustainability45:14
Thank-you45:44
Questions?46:18