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Computational Sustainability
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Linking to news and research on solving sustainability problems with computation.
Linking to news and research on solving sustainability problems with computation.

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If you’re attending the Neural Information Processing Systems (NIPS2013) conference this week in Lake Tahoe there is a workshop on Machine Learning for Sustainability with the latest research on applying Machine Learning methods to sustainability problems and how those problems bring challenges of complexity and scalability for the areas of prediction, modeling and control.
Day: Tuesday December 10, 2013
Location:  Harrah’s Glenbrook+Emerald
Scheduled Talks and Poster Sessions: schedule | mlsustws


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If you’re attending the Neural Information Processing Systems (NIPS2013) conference this week in Lake Tahoe there is a workshop on Machine Learning for Sustainability with the latest research on applying Machine Learning methods to sustainability problems and how those problems bring challenges of complexity and scalability for the areas of prediction, modeling and control.
Day: Tuesday December 10, 2013
Location:  Harrah’s Glenbrook+Emerald
Scheduled Talks and Poster Sessions: schedule | mlsustws


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The question of whether we ought to combine adaptation and mitigation finance is a hot one as we head into the 19th Conference of Parties of the +United Nations - Climate Change ( UNFCCC ). The German Development Institute just took the position that the two don't belong together. We largely disagree: For one, we side with +csiro's Brenda Lin that the benefits to be gained from thinking about them together are stark for other biodiversity, ecosystem and community health concerns: http://blog.ecoagriculture.org/2013/11/06/lin_carbon-farming-australia/
Second, that adaptation is indeed a global issue, especially as it relates to food security, and that saying adaptation finance has "local impact" only or mostly is to ignore the interconnected nature of our food system as well as human rights commitments to the right to food. See: http://blog.ecoagriculture.org/2013/10/16/world-food-day-2013/
Third, capturing synergies between adaptation and mitigation actually enhances the effectiveness of investments and interventions, rather than increasing risk, in the right contexts. We won't discover those contexts and take advantage of those synergies if we stop considering adaptation and mitigation together. See this new study in Conservation Letters:  http://landscapes.ecoagriculture.org/global_review/climatesmart_landscapes_challenges_opportunities

What do you think?

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Predicting Species Distributions across the West-Hemisphere
Knowledge of the distribution and ecological associations of a species is a crucial ingredient for successful conservation management, biodiversity and sustainability research. However, ecological systems are inherently complex, our ability to directly observe them has been limited, and the processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales.

Image: Species distribution model estimates (colorbar: relative probability of occurrence) across the West Hemisphere from massively crowdsourced eBird data

Very recently, large citizen science efforts such as eBird, a very successful crowdsourcing project by the Cornell Lab of Ornithology that engages citizen scientists and avid birders, is enabling for the first time world-wide observations of bird distributions. eBird has collected more then 100 millions of bird observations to date from as many as 100 thousand human volunteers and submissions (checklists) continue to grow with an exponential rate. With this wealth of evidence comes a plethora of challenges as the data collection and sampling designs are unstructured, follow human activities and concentrations, and are subject to observer and environmental biases. For example, sparsely populated states in the US, such as Iowa and Nevada, have very low frequency of observations whereas East and West Coast states have the highest continental counts. Furthermore, temporal variability and biases are also evident as annual submission rates peak during spring and fall migration which is the most exciting times for birders to observe multiple species.

In our AAAI paper, we propose adaptive spatiotemporal species distribution models that can exploit the uneven distribution of observations from such crowdsourcing projects and can accurately capture multiscale processes. The proposed exploratory models control for variability in the observation process and can learn ecological, environmental and climate associations that drive species distributions and migration patterns. We offer for the first time hemisphere-wide species distribution estimates of long-distance migrants (Barn Swallow, Blackpoll Warbler, and Black-throated Blue Warbler in Figure above), utilizing more then 2.25 million eBird checklists.

Until recently, most biodiversity monitoring programs that collect data have been national in scope, hindering ecological study and conservation planning for broadly distributed species. The ability to produce comprehensive year-round distribution estimates that span national borders will make it possible to better understand the ecological processes affecting the distributions of these species, assess their vulnerability to environmental perturbations such as those expected under climate change, and coordinate conservation activities.

See full blog post at the CompSustBlog: http://blog.computational-sustainability.org/?p=288
Other #CompSust Papers at AAAI2013:  http://www.aaai.org/Conferences/AAAI/2013/aaai13accepts.php#Sustainability
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#CompSust is here at #AAAI2013  in Bellevue this Thursday. Are you going?
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Computational Sustainability at #AAAI2013
The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13) convenes next week in Bellevue, Washington USA. For the third consecutive year there will be a special track on Computational Sustainability, a nascent and growing field of computing that is concerned with the application of computer science principles, methods, and tools to problems of environmental and societal sustainability.

This is not a one-way street, however, because sustainability problems force computer scientists into new theory, as well as new practice. For example, sustainability problems require extraordinary attention to solution robustness (e.g., so that a so-called optimal solution doesn’t catastrophically fail with an environmental change) and issues of uncertainty, ranging from uncertainties in environmental sensor readings to uncertainties in the budget awarded by a state legislative body for wildlife management!

The 16 papers of the Computational Sustainability (CompSust) track of AAAI (http://www.aaai.org/Conferences/AAAI/2013/aaai13accepts.php#Sustainability) cover sustainability problems in natural environment, to include various forms of resource management (e.g., species management, wildfire control), and the built environment (e.g., smart grid, building energy usage).

The #CompSust presentations are arranged in four presentation sessions, all on Thursday, July 18, 2013. These sessions are organized by AI themes of MDPs and sequential processes, optimization and search, data mining, and multi agent systems.

As in the past, the *Computing Community Consortium• is graciously supporting best paper awards for the CompSust track, which will be announced at the opening ceremony on Tuesday, July 16. 

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Approximately Optimal Planning for Invasive Species Management
If you have a very large decision making problem and want to find an approximately optimal policy, one of the best ways is often to use simulated trajectories of states, actions and utilities to learn the policy from experience. 

In many natural resource management problems running simulations is very expensive because of the complex processes involved and because of spatial interactions across a landscape. This means we need an approximate planning algorithm for MDPs that minimizes the number of calls to the simulator. Our paper at AAAI presents an algorithm for doing that.

Example Problem : Invasive Species Management
One example of a natural resource management problem with this kind of challenge is management of invasive river plants. For example, Tamarisk is an invasive plant species originating from the Middle East which has invaded over 3 million acres of land in the Western United States. It outcompetes local plants, consumes water and deposits salt into the soil. This pushes out native grass species, fundamentally changes the chemistry soil and alters an ecosystem that many other species rely on. Dropped leaves also create a dry layer of fuel that increases the risk of fire in the already fire-prone West.

Seeds from plants can spread up or down the river network leading to a huge number of reachable states. There is a choice of treatment actions available in each part of the river: we can eradicate invading plants and/or reintroduce native ones. Each treatment action has a cost, but the more expensive treatments are more effective at supplanting the invading plants. 

The Planning Problem
The planning problem is the following: Find the optimal policy for performing treatments spatially across a river network and over time in order to restore the native plant population and stay within a given budget level.

This problem can be represented as a Markov Decision Process (MDP) but it very quickly becomes intractable to solve optimally for larger problems. Ideally we want to find a policy with guarantees about how far it is from the optimal solution. PAC-MDP learning methods provide such guarantees by using long simulations to converge on a policy that is guaranteed to be within a given distance of the optimal policy with some probability (see Sidebar: What is an MDP? What does PAC-MDP mean?).

Most of the existing PAC-MDP methods look at a sequence of simulated actions and rewards and rely on revisiting states many times over and over to learn how to act optimally in those states. This does not fit the ecosystem management problem. In reality, we begin in a particular starting state S, in which the ecosystem is typically in some undesirable state far from its desired balance. The goal is to find a policy for moving to a world where S does not occur again.

Our Approach
Our paper improves upon the best approaches for doing approximate planning in large problems in two ways :

1) It obtains tighter confidence intervals on the quality of a policy by incorporating a bound on the probability of reaching additional (not-yet-visited) states. These tighter intervals mean that fewer simulations are needed. 
2) It introduces a more strategic method for choosing which state would be best to sample next by maintaining a discounted occupancy measure on all known states. 

-- Mark Crowley

Read the full post: http://blog.computational-sustainability.org/2013/07/09/approximately-optimal-planning-for-invasive-species-management/

Photo: Tamarisk (Tamarix parviflora) also known as Salt Cedar in Lower Owyhee River, OR. Photo: C.C. Shock, Oregon State University.

This is one of our series of posts on the latest research in Computational Sustainability being presented at conferences this summer. This time +Mark Crowley, a Postdoc in Computer Science at Oregon State University tells us about their new paper at AAAI in Bellevue, Washington, USA this July which has a special track on Computational Sustainability research.
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