Stochastic Environmental Research and Risk Assessment (SERRA) publishes research papers, reviews and technical notes on stochastic (probabilistic and statistic) approaches to environmental sciences and engineering, including the description and prediction of spatiotemporal natural systems under conditions of uncertainty, risk assessment, interactions of earth and atmospheric environments with people and the ecosystem, and environmental health. Its core aim is to bring together research in various fields of environmental, planetary and health sciences, providing an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of novel stochastic techniques used in different fields to the community of interested researchers. Contributions may cover scientific measurement, instrumentation and probabilistic/statistical modeling applied in various research areas including (but not limited to):
- Surface and subsurface hydrology, including stochastic hydrology, scale invariant phenomena, fractals and multifractals.
- Climate science and meteorology, including hydroclimatic variability and climate change impact assessment.
- Enviroinformatics, including data-driven approaches, machine and deep learning, and artificial neural networks.
- Public health and environmental epidemiology, including the spatiotemporal spread of infectious diseases and human exposure assessment.
- Sustainable environment, hazards and risk analysis.
- Soil contamination and remediation.
- Air pollution monitoring and control, and environmental health effects.
- Geostatistics, spatial and spatiotemporal statistics.
- Remote sensing and temporal geographical information systems.
- George Christakos
- Publishing model
- Hybrid. Learn about publishing OA with us
- 2.351 (2019)
- Impact factor
- 2.721 (2019)
- Five year impact factor
- 55 days
- Submission to first decision
- 231 days
- Submission to acceptance
- 163,734 (2019)
Investigating the spatio-temporal variations of the impact of urbanization on haze pollution using multiple indicators
Error covariance tuning in variational data assimilation: application to an operating hydrological model
Authors (first, second and last of 5)
This special issue aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal statistics and data mining on topics including, but not limited to advances in spatio-temporal modeling using geostatistics and machine learning; uncertainty quantification and representation; innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
Quantification and characterization of uncertainty are two key features of modern science-based predictions. When applied to water resources, these tasks must be able to handle many degrees of freedom, complexity of physical and (bio)chemical processes, sparsity and/or poor quality of data, and medium- and long-term variability of natural/anthropic stresses imposed on a system.
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