Advanced Statistical and Computational Techniques in Environment and Ecology Research, 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 20 February 2025
Special Issue Editors
Interests: big data; fractals; climate change; environmental evolution
Special Issues, Collections and Topics in MDPI journals
Interests: statistical and fractal models; climate change; marine ecosystem
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Earth’s environment and ecosystem are complex, multidimensional, multiscale stochastic processes in which different physical processes interact on different temporal and spatial scales. Applied mathematicians are working to extract meaningful information from large amounts of observational and simulation data using various techniques (e.g., EOF, DEA, EVA, CCA, SSA, PCA, MCMC, DFA, IPTA, Bayesian decisions, downscaling analyses, data assimilation, wavelet/framelet, spectral analyses, information entropy, and stochastic networks). Rapid advances in statistical and computational techniques have reached all aspects of the environment and ecology. At present, the ensemble statistical learning techniques are being developed to deal with the emerging big Earth datasets, which cannot be analyzed deeply using classic statistical techniques due to the size, variety, and dynamic nature of big data. In this Special Issue, we aim to collect recent results on developing and applying advanced statistical and computational techniques to reveal trends and patterns of environmental evolution, determine the links inside environment systems and ecosystems, and evaluate the uncertainty of environmental prediction to achieve sustainable development goals.
Prof. Dr. Zhihua Zhang
Prof. Dr. M. James C. Crabbe
Guest Editors
Manuscript Submission Information
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Keywords
- Interdisciplinary statistical and computational methods
- Statistical learning/machine learning
- Data assimilation and downscaling
- Fluid dynamics and its application in the atmosphere, ocean and glaciers
- Environmental modeling and diagnosis
- Dynamics analysis of ecosystems
