Disaster response agencies have been shifting from a paradigm of climate
forecasting towards one of anticipatory action: assessing not just what the
climate will be, but how it will impact specific populations, thereby enabling
proactive response and resource allocation. Machine learning models are
becoming exceptionally powerful at climate forecasting, but methodological gaps
remain in terms of facilitating anticipatory action. Here we provide an
overview of anticipatory action, review relevant applications of machine
learning, identify common challenges, and highlight areas where machine
learning can uniquely contribute to advancing disaster response for populations
most vulnerable to climate change.

灾难应对机构已从气候预测范式向预测行动范式转变，评估的不仅是气候情况，更是其对特定人群的影响，从而实现主动响应和资源分配。机器学习模型在气候预测方面变得异常强大，但在促进预测行动方面仍存在方法论上的差距。在本研究中，我们概述了预测行动，回顾了机器学习的相关应用，确定了共同的挑战，并强调了机器学习在推进对气候变化最脆弱人群的灾难应对方面的独特贡献领域。

预测行动的人工智能：超越气候预报

AI for Anticipatory Action: Moving Beyond Climate Forecasting

As opaque predictive models increasingly impact many areas of modern life,
interest in quantifying the importance of a given input variable for making a
specific prediction has grown. Recently, there has been a proliferation of
model-agnostic methods to measure variable importance (VI) that analyze the
difference in predictive power between a full model trained on all variables
and a reduced model that excludes the variable(s) of interest. A bottleneck
common to these methods is the estimation of the reduced model for each
variable (or subset of variables), which is an expensive process that often
does not come with theoretical guarantees. In this work, we propose a fast and
flexible method for approximating the reduced model with important inferential
guarantees. We replace the need for fully retraining a wide neural network by a
linearization initialized at the full model parameters. By adding a ridge-like
penalty to make the problem convex, we prove that when the ridge penalty
parameter is sufficiently large, our method estimates the variable importance
measure with an error rate of $O(\frac{1}{\sqrt{n}})$ where $n$ is the number
of training samples. We also show that our estimator is asymptotically normal,
enabling us to provide confidence bounds for the VI estimates. We demonstrate
through simulations that our method is fast and accurate under several
data-generating regimes, and we demonstrate its real-world applicability on a
seasonal climate forecasting example.

本研究提出了一种快速且灵活的方法，用于近似估算模型中某一输入变量对于做出特定预测的重要性，并证明了该方法的并发性。通过模拟和气候预测实例，证明了该方法的准确性和实用性。