论文标题
使用人工神经网络具有有限的本地数据的智能空间插值预测方法
Intelligent Spatial Interpolation-based Frost Prediction Methodology using Artificial Neural Networks with Limited Local Data
论文作者
论文摘要
霜冻的天气现象对农业构成了巨大威胁。由于最近的霜冻预测方法基于现场历史数据和传感器，因此在任何新站点中收集数据需要额外的开发和部署时间。本文的目的是消除对霜冻预测方法的现场历史数据和传感器的依赖。在本文中，提出了基于空间插值的霜冻预测方法。这些模型使用现有气象站，数字高程模型调查以及归一化差异植被指数数据的气候数据，以估计目标部位的下一个小时最低温度。提出的方法利用集合学习来提高模型的准确性。气候数据集是从新南威尔士州和澳大利亚澳大利亚首都地区的75个气象站获得的。结果表明，所提出的方法达到的检测率高达92.55％。
The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.
