This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection and false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multiobjective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices.

中文翻译：

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使用分类二元度量优化深度学习沉淀模型

这项工作介绍了一种在降水预报的背景下使用连续和分类二进制指标的组合来优化神经网络模型的方法。检测概率和虚警率是用于降水模型验证的常用指标。但是，由于基于梯度下降训练的机器学习模型不可微分，因此无法优化。我们为这些可分类的分类指数提出了一种替代的表述，并展示了如何使用它们来优化被定义为多目标优化问题的降水神经网络模型的技能。据我们所知，这是基于分类索引优化天气神经网络模型的方法的第一个建议。