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Data-driven Inference of Modulatory ...

Gonzalez, Doel Luis, II.

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Data-driven Inference of Modulatory Relationship Networks and Quantitative System Feedback Prediction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven Inference of Modulatory Relationship Networks and Quantitative System Feedback Prediction./
作者:	Gonzalez, Doel Luis, II.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	97 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Climate change. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583407
ISBN:	9781369620863

Data-driven Inference of Modulatory Relationship Networks and Quantitative System Feedback Prediction.
Gonzalez, Doel Luis, II.

Data-driven Inference of Modulatory Relationship Networks and Quantitative System Feedback Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 97 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall variability and tropical cyclone activity. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood.We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships in uencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and Dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall, including wellknown associations from prior climate knowledge, as well as promising discoveries that invite further research by the climate science community. Leveraging these, we could identify specific climate phenomena as key players to be included into a hierarchical structure that provides a quantitative prediction of specific system response behaviors. Furthermore, CHARM allows us to narrow the search space in terms of such key players, providing a smaller dimensional space and a new set of coupled features, which can lead to the training of classification models that better capture inherent climate relationships. With this information, we would have tools that can help build a more comprehensive and predictive model of the climate system, and thus provide better-targeted information for quantitative approaches geared towards the prediction of seasonal climate feedbacks.

ISBN: 9781369620863Subjects--Topical Terms:

2079509
Climate change.

Data-driven Inference of Modulatory Relationship Networks and Quantitative System Feedback Prediction.
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