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首页> 外文会议>2012 First International Conference on Agro-Geoinformatics. >Forecasting disease with 10-year optimized models: Moving toward new digital datasets
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Forecasting disease with 10-year optimized models: Moving toward new digital datasets

机译：使用10年优化模型预测疾病：朝着新的数字数据集发展

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摘要

As the pace of data availability and access to cyberinfrastructure increases, weather data inputs to practical application models have gone from point data to raster grids of varying spatial and temporal resolution. Certainly there is a benefit to widespread access of data, but transforming models developed at point locations to raster datasets is not trivial. In addition, dramatic improvements can be made to models when an extended dataset is available for testing and validation, although this is not always possible in an era of quickly changing datasets and modeling techniques. This paper examines opportunities to decrease crop disease forecasting error with longer data archives. Potato late blight in the Great Lakes region of the US is used as a test case. Model accuracy increased dramatically, especially on days conducive to disease, as more data became available and a greater familiarity with the dataset was achieved. Training and validation error fluctuated as a greater data archive became available, reinforcing the need for forecasters to better understand intraseasonal and interannual cycles that impact the success of long term agroecosystem model implementations.
机译：随着数据可用性和访问网络基础设施的步伐的加快，输入到实际应用模型中的天气数据已从点数据变为具有不同时空分辨率的栅格网格。当然，广泛访问数据会有好处，但是将在点位置处开发的模型转换为栅格数据集并非易事。此外，当扩展的数据集可用于测试和验证时，可以对模型进行重大改进，尽管在快速更改数据集和建模技术的时代并不总是可能的。本文探讨了使用更长的数据档案来减少作物病害预报误差的机会。美国大湖地区的马铃薯晚疫病用作测试案例。随着更多数据的获得和对数据集的更加熟悉，模型的准确性大大提高，尤其是在有利于疾病的日子。培训和验证错误随着可用数据档案的增加而波动，这增加了预报员对更好地了解影响长期农业生态系统模型实施成功的季节内和年际周期的需求。

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