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Statistical Weather-Impact Models: An Application of Neural Networks and Mixed Effects for Corn Production over the United States

机译:统计天气影响模型:神经网络和混合效应在美国玉米生产中的应用

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Statistical meteorological impact models are intended to represent the impact of weather on socioeconomic activities, using a statistical approach. The calibration of such models is difficult because relationships are complex and historical records are limited. Often, such models succeed in reproducing past data but perform poorly on unseen new data (a problem known as overfitting). This difficulty emphasizes the need for regularization techniques and reliable assessment of the model quality. This study illustrates, in a general way, how to extract pertinent information from weather data and exploit it in impact models that are designed to help decision-making. For a given socioeconomic activity, this type of impact model can be used to 1) study its sensitivity to weather anomalies (e.g., corn sensitivity to water stress), 2) perform seasonal forecasting (yield forecasting) for it, and 3) quantify the longer-term (several decades) impact of weather on it. The size of the training database can be increased by pooling data from various locations, but this requires statistical models that are able to use the localization information for example, mixed-effect (ME) models. Linear, neural network, and ME models are compared, using a real-world application: corn-yield forecasting over the United States. Many challenges faced in this paper may be encountered in many weather-impact analyses: these results show that much care is required when using space time data because they are often highly spatially correlated. In addition, the forecast quality is strongly influenced by the training spatial scale. For the application that is described herein, learning at the state scale is a good trade-off: it is specific to local conditions while keeping enough data for the calibration.
机译:统计气象影响模型旨在使用统计方法来表示天气对社会经济活动的影响。由于关系复杂且历史记录有限,因此此类模型的校准很困难。通常,这样的模型可以成功地复制过去的数据,但是在看不见的新数据上表现不佳(称为过拟合问题)。这个困难强调了对正则化技术和模型质量的可靠评估的需求。这项研究以一般方式说明了如何从天气数据中提取相关信息并将其用于旨在帮助决策的影响模型中。对于给定的社会经济活动,这种影响模型可用于1)研究其对天气异常的敏感性(例如,玉米对水分胁迫的敏感性),2)对其进行季节性预测(产量预测),以及3)量化天气对其的长期(几十年)影响。可以通过合并来自各个位置的数据来增加训练数据库的大小,但这需要能够使用定位信息的统计模型,例如混合效应(ME)模型。使用实际应用程序比较了线性,神经网络和ME模型:美国的玉米单产预测。在许多天气影响分析中可能会遇到本文面临的许多挑战:这些结果表明,使用时空数据时需要格外小心,因为它们通常在空间上高度相关。此外,预测质量受训练空间规模的强烈影响。对于此处描述的应用程序,在状态范围内学习是一个不错的权衡:它特定于本地条件,同时保留了足够的数据用于校准。

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