Hierarchical Classifier-Regression Ensemble for Multi-phase Non-linear Dynamic System Response Prediction: Application to Climate Analysis
Hierarchical Classifier-Regression Ensemble for Multi-phase Non-linear Dynamic System Response Prediction: Application to Climate Analysis
复制标题
多相非线性动态系统响应预测的分层分类器回归集合：在气候分析中的应用
DOI：
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发表时间：
2012
期刊：
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通讯作者：
N. Samatova
中科院分区：
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作者：
Doel L. Gonzalez;Zhengzhang Chen;I. Tetteh;Tatdow Pansombut;F. Semazzi;Vipin Kumar;A. Melechko;N. Samatova
一个动态的物理系统经常会发生相变，以响应系统参数引起的波动。例如，飓风活动是由与海洋和大气中的非线性耦合波动相关的液体-蒸汽相变引发的气候系统的反应。由于我们对高度非线性动态系统的定量知识非常贫乏，科学家经常诉诸线性回归技术，如最小绝对偏差（LAD）来学习非线性系统的响应（例如，飓风活动）根据观察到的或模拟的系统参数（例如，温度、可降水量、压力）。虽然有见地，但这些模型仍然提供有限的可预测性，并且旨在捕获非线性行为（如逐步回归）的替代方案在本质上往往是有争议的。在本文中，我们假设，缺乏可预测性的主要原因之一是治疗的一个固有的多相系统是相less. To弥合这一差距，我们提出了一种混合的方法，首先预测的相位系统是在，然后估计系统的响应幅度使用的回归模型优化为这个阶段。我们的方法是专为系统的特点是多变量的时空数据从观察，模拟，或两者兼而有之。
A dynamic physical system often undergoes phase transitions in response to fluctuations induced on system parameters. For example, hurricane activity is the climate system's response initiated by a liquid-vapor phase transition associated with non-linearly coupled fluctuations in the ocean and the atmosphere. Because our quantitative knowledge about highly non-linear dynamic systems is very meager, scientists often resort to linear regression techniques such as Least Absolute Deviation (LAD) to learn the non-linear system's response (e.g., hurricane activity) from observed or simulated system's parameters (e.g., temperature, precipitable water, pressure). While insightful, such models still offer limited predictability, and alternatives intended to capture non-linear behaviors such as Stepwise Regression are often controversial in nature. In this paper, we hypothesize that one of the primary reasons for lack of predictability is the treatment of an inherently multi-phase system as being phase less. To bridge this gap, we propose a hybrid approach that first predicts the phase the system is in, and then estimates the magnitude of the system's response using the regression model optimized for this phase. Our approach is designed for systems that could be characterized by multi-variate spatio-temporal data from observations, simulations, or both.