论文标题
使用时间序列的气候预测和深度学习来预测未来的蚊子幼虫栖息地
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep Learning
论文作者
论文摘要
由于气候变化，预计蚊子栖息地范围会扩大。该研究旨在通过分析蚊子幼虫的首选生态条件来鉴定未来的蚊子栖息地。在与大气记录和幼虫观察结果组装在一起的数据集后，训练了神经网络，以预测生态输入中的幼虫计数。时间序列预测是对这些变量进行的，气候预测将传递到初始的深度学习模型中，以产生特定于位置的幼虫丰度预测。结果支持蚊子扩散区域生态系统驱动的变化的概念，高海拔区域尤其经历了对蚊子侵扰的敏感性的增加。
Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions in particular experiencing an increase in susceptibility to mosquito infestation.
