Toward data-driven, semi-automatic inference of phenomenological physical models: Application to eastern sahel rainfall

Saurabh V. Pendse, Isaac K. Tetteh, Fredrick Semazzi, Vipin Kumar, Nagiza F. Samatova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

First-principles based predictive understanding of complex, dynamic physical phenomena, such as regional precipitation or hurricane intensity and frequency, is quite limited due to the lack of complete phenomenological models underlying their physics. To address this gap, hypothesis-driven, manually-constructed, conceptual hurricane models and models for regional-scale precipitation extremes have been emerging. To complement both approaches, we propose a methodology for data-driven, semi-automatic inference of plausible phenomenological models and apply it to derive the model for eastern Sahel rainfall, an important factor for socioeconomic growth and development of this region. At its core, our methodology derives cause-effect relationships using the Lasso multivariate regression model and quantifies compound affect that the complex interplay among the key predictors at their prominent temporal phases plays on the response (rainfall). Specifically, we propose methods for (a) detecting and ranking predictors' prominent temporal phases, (b) optimizing the regularization penalty, (c) assessing predictor statistical significance, (d) performing impact analysis of data normalization on model inference, and (e) calculating the Expected Causality Impact (ECI) score to quantify impact analysis. The culmination of this study is the plausible phenomenological model of the eastern Sahel seasonal rainfall and quantified key climate drivers involved in the rainfall variability at different time lags. To the best of our knowledge, this is the first phenomenological model of this phenomenon; several of its components are consistent with the known evidence from literature.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
Pages35-46
Number of pages12
StatePublished - Dec 1 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Other

Other12th SIAM International Conference on Data Mining, SDM 2012
CountryUnited States
CityAnaheim, CA
Period4/26/124/28/12

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