Preface to the thematic issue on Environmental Data Science. Applications to air quality and water cycle
Abstract
This special issue harvested a set of papers that provide a useful perspective of how Data Science tackles nonlinear, spatio-temporal environmental phenomena using a variety of data sources (numeric variables, qualitative, ordinal, time series, videos, smart sensor data) for both descriptive and predictive processes. The included papers employ a variety of techniques, ranging from classical statistical methods to innovative pre-processing methods or hybrid machine learning methods to extract knowledge from data. The selection of papers for this issue was done with a rigorous blind peer-review process and high rate of rejection. The issue originally received 29 submissions, and the 8 papers most favorably evaluated by reviewers were selected. The reviewing process involved more than 170 reviewers. The selected papers provide a nice overview of research conducted in multiple countries, including international collaborations and some collaboration between academia and corporations.
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Published as:
K. Gibert,
J. S.Horsburgh,
I. N.Athanasiadis,
G. Holmes,
Preface to the thematic issue on Environmental Data Science. Applications to air quality and water cycle,
Environmental Modelling and Software, 106:1-3,
2018, doi:10.1016/j.envsoft.2018.03.020.
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