Collaborating Authors

environmental sciences

Using machine learning to understand climate change Artificial Intelligence Research


Methane is a potent greenhouse gas that is being added to the atmosphere through both natural processes and human activities, such as energy production and agriculture. To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs--both natural and human--as well as the outputs. They also need to know how long methane resides in the atmosphere. For more information see the IDTechEx report on Smart City Opportunities: Infrastructure, Systems, Materials 2019-2029.

Using machine learning to understand climate change: Researchers find global ocean methane emissions dominated by shallow coastal waters


To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs -- both natural and human -- as well as the outputs. They also need to know how long methane resides in the atmosphere. To help develop this understanding, Tom Weber, an assistant professor of earth and environmental sciences at the University of Rochester; undergraduate researcher Nicola Wiseman '18, now a graduate student at the University of California, Irvine; and their colleague Annette Kock at the GEOMAR Helmholtz Centre for Ocean Research in Germany, used data science to determine how much methane is emitted from the ocean into the atmosphere each year. Their results, published in the journal Nature Communications, fill a longstanding gap in methane cycle research and will help climate scientists better assess the extent of human perturbations.

Light, A Possible Solution For A Sustainable Artificial Intelligence - Liwaiwai


We are currently witnessing a rapidly growing adoption of artificial intelligence (AI) in our everyday lives, which has the potential to translate into a variety of societal changes, including improvements to economy, better living conditions, easier access to education, well-being, and entertainment. Such a much anticipated future, however, is tainted with issues related to privacy, explainability, accountability, to name a few, that constitute a threat to the smooth adoption of AI, and which are at the center of various debates in the media. A perhaps more worrying aspect is related to the fact that current AI technologies are completely unsustainable, and unless we act quickly, this will become the major obstacle to the wide adoption of artificial intelligence in society. But before diving into the issues of sustainability of AI, what is AI? AI aims at building artificial agents capable of sensing and reasoning about their environment, and ultimately learning by interacting with it. Machine Learning (ML) is an essential component of AI, which makes it possible to establish correlations and causal relationships among variables of interest from data and prior knowledge of the processes characterizing the agent's environment.

Express delivery: use drones not trucks to cut carbon emissions, experts say

The Guardian - Business

Tue 13 Feb 2018 11.00 EST Last modified on Tue 13 Feb 2018 11.01 EST Drones invoke varying perceptions, from fun gadget to fly in the park to deadly military weapons. In the future, they may even be viewed as a handy tool in the battle to fight climate change. Greenhouse gas emissions from the tra...

pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data Artificial Intelligence

Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the \emph{spatiotemporal (ST) causal pathways} for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis, (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high, and (3) the \emph{ST causal pathways} are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}. First, \emph{Pattern mining} helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as "causers". Then, \emph{Bayesian learning} carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors, in three regions of China from 01-Jun-2013 to 19-Dec-2015. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.

Multi-Period Flexibility Forecast for Low Voltage Prosumers Artificial Intelligence

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

Drones and AI help stop poaching in Africa


Several organizations are already using drones to fight poaching, but the Lindbergh Foundation is taking it one step further. The environmental non-profit has joined forces with Neurala in order to use the company's deep learning neural network AI to boost the capabilities of the drones in its Air Shepherd program. Neurala taught its technology what elephants, rhinos and poachers look like, so it can accurately pinpoint and mark them in videos. It will now put the AI to work sifting through all the footage the foundation's drones beam back in real time, including infrared footage taken at night. The AI's job is to pore over these videos and quickly identify the presence of poachers to prevent them from even reaching the animals' herds.

The Morning After: Friday, March 17 2017


The second man to set foot on the moon thinks colonizing Mars is humanity's destiny. Buzz Aldrin, Apollo 11 astronaut, may now be 87, but he's keeping his mind focused on the next space frontier. For decades now, he's thought about how to get astronauts to Mars, becoming more vocal about his plans in recent years. He's also a fan of virtual reality as a medium to communicate his vision: He partnered with NASA to build a Mars Hololens experience last year, and now he's hosting a 10-minute VR experience that walks you through his vision of how to get to Mars. Now it's up to CongressTrump's budget proposal means big cuts for NASA, climate change programs The president's proposed 2018 budget has been revealed.

Technology could DESTROY humanity claims Stephen Hawking

Daily Mail - Science & tech

Technology must be controlled in order to safeguard the future of humanity, Stephen Hawking has warned. The physicist, who has spoken out about the dangers of artificial intelligence in the past, says a'world government' could be our only hope. He says our'logic and reason' could be the only way to defeat the growing threat of nuclear or biological war. We are living through the most dangerous time in the history of the human race, according to Professor Stephen Hawking. 'Since civilisation began, aggression has been useful inasmuch as it has definite survival advantages,' he told The Times. 'It is hard-wired into our genes by Darwinian evolution.

It's Time to Take the Gaia Hypothesis Seriously - Facts So Romantic


Can a planet be alive? Lynn Margulis, a giant of late 20th-century biology, who had an incandescent intellect that veered toward the unorthodox, thought so. She and chemist James Lovelock together theorized that life must be a planet-altering phenomenon and the distinction between the "living" and "nonliving" parts of Earth is not as clear-cut as we think. Many members of the scientific community derided their theory, called the Gaia hypothesis, as pseudoscience, and questioned their scientific integrity. But now Margulis and Lovelock may have their revenge. Recent scientific discoveries are giving us reason to take this hypothesis more seriously. At its core is an insight about the relationship between planets and life that has changed our understanding of both, and is shaping how we look for life on other worlds.