论文标题
基于模式的人口暴发预测
Pattern-Based Prediction of Population Outbreaks
论文作者
论文摘要
昆虫暴发是造成经济和生态损害的森林和农业生态系统中的生物干扰。这种现象取决于各种生物学和物理因素。该问题的复杂性和实际重要性使预测暴发的问题成为最近研究的重点。在这里，我们提出了基于模式的预测（PBP）方法来预测人口暴发。它基于警报区域的过程，结合了机器学习的元素。它使用先前时间序列值的信息，该值是爆发事件之前的预测因素，这在监测害虫物种时可能很有用。我们使用模拟数据集和实时序列数据来说明方法，通过监测巴西南部的小麦农作物中的蚜虫获得的方法。在使用随机模型实施的仿真研究中，我们获得了$ 84.6 \％$的平均测试准确性，用于使用真实数据集预测暴发的$ 95.0 \％$。这显示了PBP方法在预测人口动态爆发方面的可行性。我们针对已建立的最先进的机器学习方法，即支持向量机，深度神经网络，长期记忆和随机森林进行了基准测试。 PBP方法产生了竞争性能，在大多数比较中与较高的真实阳性率有关，同时能够提供可解释性而不是黑盒方法。这是对当前最新机器学习工具的改进，尤其是在非专家使用的情况下，例如旨在使用定量方法进行害虫监测的生态学家。我们提供开源代码，通过\ texttt {pypbp}软件包在Python中实现PBP方法，该软件包可以直接从Python软件包索引服务器下载或通过\ url {https://pypbp-documentation.io}
Insect outbreaks are biotic disturbances in forests and agroecosystems that cause economic and ecological damage. This phenomenon depends on a variety of biological and physical factors. The complexity and practical importance of the issue have made the problem of predicting outbreaks a focus of recent research. Here, we propose the Pattern-Based Prediction (PBP) method for predicting population outbreaks. It is based on the Alert Zone Procedure, combined with elements from machine learning. It uses information on previous time series values that precede an outbreak event as predictors of future outbreaks, which can be useful when monitoring pest species. We illustrate the methodology using simulated datasets and real time series data obtained by monitoring aphids in wheat crops in Southern Brazil. We obtained an average test accuracy of $84.6\%$ in the simulation studies implemented with stochastic models, and $95.0\%$ for predicting outbreaks using the real dataset. This shows the feasibility of the PBP method in predicting outbreaks in population dynamics. We benchmarked our results against established state-of-the-art machine learning methods, namely Support Vector Machines, Deep Neural Networks, Long Short Term Memory and Random Forests. The PBP method yielded a competitive performance, associated with higher true-positive rates in most comparisons, while being able to provide interpretability rather than being a black-box method. This is an improvement over current state-of-the-art machine learning tools, especially when being used by non-specialists, such as ecologists aiming to use a quantitative approach for pest monitoring. We provide open-source code to implement the PBP method in Python, through the \texttt{pypbp} package, which may be directly downloaded from the Python Package Index server or accessed through \url{https://pypbp-documentation.readthedocs.io}
