Articles | Volume 22, issue 1
Research article
13 Jan 2015
Research article |  | 13 Jan 2015

On the data-driven inference of modulatory networks in climate science: an application to West African rainfall

D. L. González II, M. P. Angus, I. K. Tetteh, G. A. Bello, K. Padmanabhan, S. V. Pendse, S. Srinivas, J. Yu, F. Semazzi, V. Kumar, and N. F. Samatova

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

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Bühlmann, P.: Causal statistical inference in high dimensions, Math. Meth. of OR, 77, 357–370, 2013.
Short summary
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 fall into two categories: well-known associations from prior knowledge, and putative links that invite further research.