A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

By James H. Faghmous, Vipin Kumar. 2014

212 citations recorded by the source corpus.

Abstract Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.

Subfields: Artificial Intelligence, Global and Planetary Change, Information Systems and Management, Statistics and Probability, AI for Physical Sciences

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Citations in the corpus

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (2022, background): “Data science is important in many areas (Romero & Ventura, 2013; Bolyen et al., 2019; Faghmous & Kumar, 2014), but requires programming proficiency in specialized libraries, thus posing substantial barriers to lay users.”
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “These efforts have been complemented with recent review papers [8], [31], [32], [33], workshops (e.”
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, background): “As previously investigated in diverse disciplines (Faghmous and Kumar, 2014; Fischer et al., 2006; Wagner and Rondinelli, 2016), theory-guided ML can be developed through a variety of approaches (Karpatne et al., 2017).”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “, [11], [12]) and special issues of journals and magazines (e.”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “Early results in isolated and relatively simple scenarios are promising, and the expectations are rising for this paradigm to accelerate scientific discovery and help address some of the biggest challenges that are facing humanity in terms of climate [87], health [280], and food security [129].”
Big Data Meet Green Challenges: Big Data Toward Green Applications (2016, background): “Faghmous and Kumar [10] discussed some challenges and opportunities on mining large climate data, and propose a data science paradigm theory guided by other scientific theories to constrain both the big data techniques as well as the relevant interpretation process to extract accurate insight from l”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “Early results in isolated and relatively simple scenarios have been promising, and the expectations are rising for this paradigm to accelerate scientific discovery and help address some of the biggest challenges that are facing humanity in terms of climate [75], health [259], and food security [118]”