Wildfire forecasting problems usually rely on complex grid-based mathematical
models, mostly involving Computational fluid dynamics(CFD) and Celluar
Automata, but these methods have always been computationally expensive and
difficult to deliver a fast decision pattern. In this paper, we provide machine
learning based approaches that solve the problem of high computational effort
and time consumption. This paper predicts the burning duration of a known
wildfire by RF(random forest), KNN, and XGBoost regression models and also
image-based, like CNN and Encoder. Model inputs are based on the map of
landscape features provided by satellites and the corresponding historical fire
data in this area. This model is trained by happened fire data and landform
feature maps and tested with the most recent real value in the same area. By
processing the input differently to obtain the optimal outcome, the system is
able to make fast and relatively accurate future predictions based on landscape
images of known fires.

用机器学习方法预测森林火灾持续时间，通过卫星提供的地貌特征地图和历史火灾数据训练模型，并基于已知火灾的地貌图像进行快速准确的未来预测。

利用回归方法估计火灾持续时间

Estimating fire Duration using regression methods

Dynamic algorithm selection aims to exploit the complementarity of multiple
optimization algorithms by switching between them during the search. While
these kinds of dynamic algorithms have been shown to have potential to
outperform their component algorithms, it is still unclear how this potential
can best be realized. One promising approach is to make use of landscape
features to enable a per-run trajectory-based switch. Here, the samples seen by
the first algorithm are used to create a set of features which describe the
landscape from the perspective of the algorithm. These features are then used
to predict what algorithm to switch to.
In this work, we extend this per-run trajectory-based approach to consider a
wide variety of potential points at which to perform the switch. We show that
using a sliding window to capture the local landscape features contains
information which can be used to predict whether a switch at that point would
be beneficial to future performance. By analyzing the resulting models, we
identify what features are most important to these predictions. Finally, by
evaluating the importance of features and comparing these values between
multiple algorithms, we show clear differences in the way the second algorithm
interacts with the local landscape features found before the switch.

本研究通过使用局部景观特征来预测何时切换算法，扩展了基于每次运行的轨迹的多算法选择方法，并分析了多个算法之间的局部景观特征交互的差异。

判断是否需要切换算法：基于轨迹特征预测算法切换的收益

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features

The global population at risk of mosquito-borne diseases such as dengue,
yellow fever, chikungunya and Zika is expanding. Infectious disease models
commonly incorporate environmental measures like temperature and precipitation.
Given increasing availability of high-resolution satellite imagery, here we
consider including landscape features from satellite imagery into infectious
disease prediction models. To do so, we implement a Convolutional Neural
Network (CNN) model trained on Imagenet data and labelled landscape features in
satellite data from London. We then incorporate landscape features from
satellite image data from Pakistan, labelled using the CNN, in a well-known
Susceptible-Infectious-Recovered epidemic model, alongside dengue case data
from 2012-2016 in Pakistan. We study improvement of the prediction model for
each of the individual landscape features, and assess the feasibility of using
image labels from a different place. We find that incorporating
satellite-derived landscape features can improve prediction of outbreaks, which
is important for proactive and strategic surveillance and control programmes.

此研究探讨了卫星图像中的景观特征在感染病预测模型中的应用，通过采用卷积神经网络从卫星数据中提取并标记的景观特征，结合巴基斯坦的登革热病例数据与 SIR 疫情模型，发现卫星图像中的景观特征能够显著提高疾病爆发的预测能力，对主动和战略性的监测和控制计划非常重要。

用于改善媒介传播疾病预测的深层景观特征

Deep Landscape Features for Improving Vector-borne Disease Prediction

We present a simple model to study L\'{e}vy-flight foraging in a finite
landscape with countable targets. In our approach, foraging is a step-based
exploratory random search process with a power-law step-size distribution $P(l)
\propto l^{-\mu}$. We find that, when the termination is regulated by a finite
number of steps $N$, the optimum value of $\mu$ that maximises the foraging
efficiency can vary substantially in the interval $\mu \in (1,3)$, depending on
the landscape features (landscape size and number of targets). We further
demonstrate that subjective returning can be another significant factor that
affects the foraging efficiency in such context. Our results suggest that
L\'{e}vy-flight foraging may arise through an interaction between the
environmental context and the termination of exploitation, and particularly
that the number of steps can play an important role in this scenario which is
overlooked by most previous work. Our study not only provides a new perspective
on L\'{e}vy-flight foraging, but also opens new avenues for investigating the
interaction between foraging dynamics and environment as well as offers a
realistic framework for analysing animal movement patterns from empirical data.

我们使用一个简单的模型研究了有限的景观中基于 Lévy-flight 策略的觅食行为，发现终止搜索的步数会对策略的最优参数范围造成显著的影响，并且主观的返回行为在这种情况下也是一个重要的因素。我们的研究不仅为 Lévy-flight 策略提供了新的视角，同时为分析动物移动模式提供了一个现实的框架。