Each year, wildfires destroy larger areas of Spain, threatening numerous
ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour
of individuals is unpredictable. However, atmospheric and environmental
variables affect the spread of wildfires, and they can be analysed by using
deep learning. In order to mitigate the damage of these events we proposed the
novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic
and ecological impact of a wildfire, assisting managers resource allocation and
decision making for dangerous regions in Spain, Castilla y Le\'on and
Andaluc\'ia. The WAM uses a residual-style convolutional network architecture
to perform regression over atmospheric variables and the greenness index,
computing necessary resources, the control and extinction time, and the
expected burnt surface area. It is first pre-trained with self-supervision over
100,000 examples of unlabelled data with a masked patch prediction objective
and fine-tuned using 311 samples of wildfires. The pretraining allows the model
to understand situations, outclassing baselines with a 1,4%, 3,7% and 9%
improvement estimating human, heavy and aerial resources; 21% and 10,2% in
expected extinction and control time; and 18,8% in expected burnt area. Using
the WAM we provide an example assessment map of Castilla y Le\'on, visualizing
the expected resources over an entire region.

使用深度学习分析环境变量，提前预测西班牙野火对经济和生态的影响，为危险地区的经理资源分配和决策制定提供帮助。

西班牙山火：基于卫星图像处理和大气信息的新型山火风险评估模型

Spain on Fire: A novel wildfire risk assessment model based on image  satellite processing and atmospheric information

Researchers typically resort to numerical methods to understand and predict
ocean dynamics, a key task in mastering environmental phenomena. Such methods
may not be suitable in scenarios where the topographic map is complex,
knowledge about the underlying processes is incomplete, or the application is
time critical. On the other hand, if ocean dynamics are observed, they can be
exploited by recent machine learning methods. In this paper we describe a
data-driven method to predict environmental variables such as current velocity
and sea surface height in the region of Santos-Sao Vicente-Bertioga Estuarine
System in the southeastern coast of Brazil. Our model exploits both temporal
and spatial inductive biases by joining state-of-the-art sequence models (LSTM
and Transformers) and relational models (Graph Neural Networks) in an
end-to-end framework that learns both the temporal features and the spatial
relationship shared among observation sites. We compare our results with the
Santos Operational Forecasting System (SOFS). Experiments show that better
results are attained by our model, while maintaining flexibility and little
domain knowledge dependency.

在本文中，我们使用先进的机器学习方法，包括序列模型（LSTM 和 Transformers）和关系模型（图神经网络），建立了一个在巴西东南海岸 Santos-Sao Vicente-Bertioga 海湾地区可以预测洋流速度和海面高度等环境变量的数据驱动模型，并将我们的结果与 Santos 操作预测系统进行了比较，证明了我们的模型可以在保持一定的灵活性和少量领域知识依赖性的同时实现更好的预测效果。

动态图神经网络建模海洋变量

Modeling Oceanic Variables with Dynamic Graph Neural Networks

As the costs of sensors and associated IT infrastructure decreases - as
exemplified by the Internet of Things - increasing volumes of observational
data are becoming available for use by environmental scientists. However, as
the number of available observation sites increases, so too does the
opportunity for data quality issues to emerge, particularly given that many of
these sensors do not have the benefit of official maintenance teams. To realise
the value of crowd sourced 'Internet of Things' type observations for
environmental modelling, we require approaches that can automate the detection
of outliers during the data modelling process so that they do not contaminate
the true distribution of the phenomena of interest. To this end, here we
present a Bayesian deep learning approach for spatio-temporal modelling of
environmental variables with automatic outlier detection. Our approach
implements a Gaussian-uniform mixture density network whose dual purposes -
modelling the phenomenon of interest, and learning to classify and ignore
outliers - are achieved simultaneously, each by specifically designed branches
of our neural network. For our example application, we use the Met Office's
Weather Observation Website data, an archive of observations from around 1900
privately run and unofficial weather stations across the British Isles. Using
data on surface air temperature, we demonstrate how our deep mixture model
approach enables the modelling of a highly skilled spatio-temporal temperature
distribution without contamination from spurious observations. We hope that
adoption of our approach will help unlock the potential of incorporating a
wider range of observation sources, including from crowd sourcing, into future
environmental models.

通过引入 Bayesian 深度学习的高斯 - 均匀混合密度网络，实现对环境变量在时空域上的建模和异常值检测，为处理来自 ' 物联网 ' 观测点数据的环境建模提供了途径。

混合密度深度网络用于众包天气数据的异常值校正内插

A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data

Machine learning algorithms find frequent application in spatial prediction
of biotic and abiotic environmental variables. However, the characteristics of
spatial data, especially spatial autocorrelation, are widely ignored. We
hypothesize that this is problematic and results in models that can reproduce
training data but are unable to make spatial predictions beyond the locations
of the training samples. We assume that not only spatial validation strategies
but also spatial variable selection is essential for reliable spatial
predictions. We introduce two case studies that use remote sensing to predict
land cover and the leaf area index for the "Marburg Open Forest", an open
research and education site of Marburg University, Germany. We use the machine
learning algorithm Random Forests to train models using non-spatial and spatial
cross-validation strategies to understand how spatial variable selection
affects the predictions. Our findings confirm that spatial cross-validation is
essential in preventing overoptimistic model performance. We further show that
highly autocorrelated predictors (such as geolocation variables, e.g. latitude,
longitude) can lead to considerable overfitting and result in models that can
reproduce the training data but fail in making spatial predictions. The problem
becomes apparent in the visual assessment of the spatial predictions that show
clear artefacts that can be traced back to a misinterpretation of the spatially
autocorrelated predictors by the algorithm. Spatial variable selection could
automatically detect and remove such variables that lead to overfitting,
resulting in reliable spatial prediction patterns and improved statistical
spatial model performance. We conclude that in addition to spatial validation,
a spatial variable selection must be considered in spatial predictions of
ecological data to produce reliable predictions.

本文证明了在生态数据的空间预测中，除了空间验证之外，空间变量选择也必须考虑，以产生可靠的预测结果。使用 “随机森林” 机器学习算法，作者通过两个案例研究了非空间和空间交叉验证策略，并发现高度自相关的预测变量可能导致过拟合，从而产生错误的空间预测模式。