Stochastic Environmental Research and Risk Assessment

Aims & Scope

Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems.

The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers.

Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.6
2024	3.90

SJR (SCImago Journal Rank)

Year	Value
2024	0.885

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	90

Journal Rank

Year	Value
2024	5843

Journal Citation Indicator

Year	Value
2024	3033

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Engineering and Environmental Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

  Citation: 424

  Authors: Rahim, Mohammad Taghi, Jan

• Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method

  Citation: 388

  Authors: Mahyat Shafapour, Biswajeet, Mustafa Neamah

• Drought forecasting using stochastic models

  Citation: 374

  Authors: A. K., V. R.

• Maximum likelihood Bayesian averaging of uncertain model predictions

  Citation: 350

  Authors: S. P.

• Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

  Citation: 348

  Authors: Jasper A., Cajo J. F., Hoshin V., Bruce A.

• A simulation of probabilistic wildfire risk components for the continental United States

  Citation: 347

  Authors: Mark A., Charles W., Isaac C., Karin L., Karen C.

• Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP

  Citation: 346

  Authors: Qiang, Jianzhong, Chao, Lixiang, Jun

• Monthly runoff forecasting based on LSTM–ALO model

  Citation: 301

  Authors: Xiaohui, Chen, Xiaohui, Yanbin, Rana

• Geometric approach to statistical analysis on the simplex

  Citation: 293

  Authors: V., J. J.

• A machine learning forecasting model for COVID-19 pandemic in India

  Citation: 273

  Authors: R., Jyotir Moy, Aboul Ella

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