36,580 research outputs found
AutoML-based Almond Yield Prediction and Projection in California
Almonds are one of the most lucrative products of California, but are also
among the most sensitive to climate change. In order to better understand the
relationship between climatic factors and almond yield, an automated machine
learning framework is used to build a collection of machine learning models.
The prediction skill is assessed using historical records. Future projections
are derived using 17 downscaled climate outputs. The ensemble mean projection
displays almond yield changes under two different climate scenarios, along with
two technology development scenarios, where the role of technology development
is highlighted. The mean projections and distributions provide insightful
results to stakeholders and can be utilized by policymakers for climate
adaptation.Comment: Submitted to Tackling Climate Change with Machine Learning: workshop
at NeurIPS 202
Personalizing Sustainable Agriculture with Causal Machine Learning
To fight climate change and accommodate the increasing population, global
crop production has to be strengthened. To achieve the "sustainable
intensification" of agriculture, transforming it from carbon emitter to carbon
sink is a priority, and understanding the environmental impact of agricultural
management practices is a fundamental prerequisite to that. At the same time,
the global agricultural landscape is deeply heterogeneous, with differences in
climate, soil, and land use inducing variations in how agricultural systems
respond to farmer actions. The "personalization" of sustainable agriculture
with the provision of locally adapted management advice is thus a necessary
condition for the efficient uplift of green metrics, and an integral
development in imminent policies. Here, we formulate personalized sustainable
agriculture as a Conditional Average Treatment Effect estimation task and use
Causal Machine Learning for tackling it. Leveraging climate data, land use
information and employing Double Machine Learning, we estimate the
heterogeneous effect of sustainable practices on the field-level Soil Organic
Carbon content in Lithuania. We thus provide a data-driven perspective for
targeting sustainable practices and effectively expanding the global carbon
sink.Comment: Accepted for publication and spotlight presentation at Tackling
Climate Change with Machine Learning: workshop at NeurIPS 202
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery
Information on urban tree canopies is fundamental to mitigating climate
change [1] as well as improving quality of life [2]. Urban tree planting
initiatives face a lack of up-to-date data about the horizontal and vertical
dimensions of the tree canopy in cities. We present a pipeline that utilizes
LiDAR data as ground-truth and then trains a multi-task machine learning model
to generate reliable estimates of tree cover and canopy height in urban areas
using multi-source multi-spectral satellite imagery for the case study of
Chicago.Comment: 4 pages, 4 figures, Submitted to Tackling Climate Change with Machine
Learning: workshop at NeurIPS 202
An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
Despite the importance of quantifying how the spatial patterns of extreme
precipitation will change with warming, we lack tools to objectively analyze
the storm-scale outputs of modern climate models. To address this gap, we
develop an unsupervised machine learning framework to quantify how storm
dynamics affect precipitation extremes and their changes without sacrificing
spatial information. Over a wide range of precipitation quantiles, we find that
the spatial patterns of extreme precipitation changes are dominated by spatial
shifts in storm regimes rather than intrinsic changes in how these storm
regimes produce precipitation.Comment: 14 Pages, 9 Figures, Accepted to "Tackling Climate Change with
Machine Learning: workshop at NeurIPS 2022". arXiv admin note: text overlap
with arXiv:2208.1184
- …