Stochastic Environmental Research and Risk Assessment - Call for Papers: Artificial Intelligence and High-Performance Computing Algorithms for Environmental Research and Risk Quantification

Scope

Artificial Intelligence algorithms have widely been applied for describing processes and creating predictive models in Environmental Research. Despite the fast evolution of mathematical frameworks and algorithmic settings, no golden algorithm has generic applicability to all data-sets and performance metrics. Numerous Artificial Intelligence (AI) algorithms have been outperforming past ones; however, empirical rigor is not following such advancements [1–3]. Researchers argue the accuracy of iterative algorithms and the results obtained for a particular problem are not always reproducible [4,5]. Such criticism on artificial intelligence algorithms, which though efficient, lack a solid theoretical background, is aligned to the scope of this special collection of papers in “Stochastic Environmental Research and Risk Assessment” (SERRA) for generic, efficient, and theoretically sound methods [1,2,6].

Furthermore, in cases when a large volume of data is involved in Environmental Research, the usage of a supercomputer is vital, in order to investigate which algorithm performs better. Accordingly, one drawback of approximation algorithms is tuning the models’ hyper-parameters. For instance, in Artificial Neural Networks, parameters such as learning rate, momentum, number of layers and neurons, vastly affect the accuracy as well as generalization capabilities of a model, and a heuristic search is necessary to obtain robust results. Henceforth, computing power is essential to optimise the applied models as well as their hyperparameters extensively. Scaling up the involved algorithms is also important in exploiting the computing power optimally.

We invite contributions involving Machine Learning and High-Performance Computing Algorithms, for Environmental Science. The novelty should reflect a major advancement in the methodological part of Computational Statistics, Machine Learning, Artificial Intelligence, High-Performance Computing, Predictive modelling, Global optimization, or Data-centric algorithms. The fields of application include Earthquake engineering, Geotechnics, Industrial wastes, Bio-friendly materials, Hydroclimatic Risk, Extreme Rainfalls, Water Resources, Climate change and similar topics as described here (this opens in a new tab). 

Keywords— Computational Statistics, Machine Learning, Artificial Intelligence, High-Performance Computing, Predictive modelling, Global optimization, Data-centric algorithms, Earthquake engineering, Geotechnics, Industrial wastes, Bio-friendly materials, Hydroclimatic Risk, Extreme Rainfalls, Water Resources, Climate change

Timeline
The deadline for submissions is 30 June 2024.

How to Submit
Manuscripts should be original and written in English. Submission requires that the manuscript has not been submitted for review or publication elsewhere and that it will not be submitted elsewhere while the review process is underway. All papers go through peer review by at least two experts.

Papers should be submitted online here. (this opens in a new tab) Please indicate that your manuscript belongs to the ‘AI and High-Performance Computer Algorithms’ special issue in the ‘Additional Information’ tab during the submission process. Details about the preparation of the manuscript can be obtained from the journal's webpage at https://www.springer.com/journal/477/submission-guidelines (this opens in a new tab).

Stochastic Environmental Research and Risk Assessment is a hybrid open access journal. 

Authors can opt to make their research Open Access (OA) with Open Choice if they wish, in which case a fee is payable. More information can be found here. (this opens in a new tab) Visit Springer Nature’s Open Access funding & support services (this opens in a new tab) for information about Transformative Agreements, research funders, and institutions that provide funding for Article Processing Charges.

Guest Editors

Nikolaos Bakas (Lead Guest Editor) is a Senior Data Scientist at the National Infrastructures for Research and Technology - GRNET and an Assistant professor at The American College of Greece, Deree. His research interests comprise the fundamental mathematical modeling of machine learning algorithms, as well as applications in a variety of thematic areas, such as predictive modeling for tabular datasets and time series, hyperparameter tuning, recommender systems, and large language models. Apart from research, he has been a researcher for various industrial applications of AI. https://github.com/nbakas (this opens in a new tab); nibas@grnet.gr (this opens in a new tab); nbakas@acg.edu (this opens in a new tab)

Savvas Chatzichristofis is a Vice-Rector and Professor of AI at Neapolis University Pafos, where he directs the Intelligent Systems Laboratory. His research focuses on the intersection of AI, computer vision, and robotics, and he has been involved in multiple R&D projects funded by European and National organizations. He holds a Diploma (M.Eng.) and a Ph.D. in Computer Vision from Democritus University and has served as a special scientist, visiting lecturer, and researcher at various institutions. He has received numerous distinctions, grants, scholarships, and awards for his contributions, and has published extensively in prestigious academic journals. More info at https://chatzichristofis.info/ (this opens in a new tab); s.chatzichristofis@nup.ac.cy (this opens in a new tab)

Stergios Emmanouil is a Postdoctoral Research Associate at the Eversource Energy Center of the University of Connecticut (Storrs, United States). He holds a 5-year Diploma in Civil Engineering from the University of Patras (Patras, Greece), a Master of Science (M.Sc.) in Hydraulic Engineering from the Delft University of Technology (Delft, the Netherlands), and a Doctor of Philosophy (Ph.D.) in Civil and Environmental Engineering from the University of Connecticut (Storrs, United States). His current research focuses on, but is not limited to, the development and application of statistical and stochastic approaches toward the modelling of natural processes and engineering systems, for risk assessment, design, and control. For more information, please visit https://ucwater.engr.uconn.edu/person/stergios-emmanouil/ (this opens in a new tab); stergios.emmanouil@uconn.edu (this opens in a new tab)

Stelios Karozis is a Research Associate at Environmental Research Laboratory at NCSR “Demokritos”. He holds a Chemical Engineer diploma and a MSc degree in Computational Mechanics from National Technical University of Athens, a Doctorate degree in Molecular Simulation from University of Crete and a MSc degree in Data Science from University of Peloponnese. His research interests are focus in modelling physical systems using deterministic, stochastic and data-driven techniques. He is also an expert in High Performance Computing (HPC) research and development with experience in the installation and management of HPC infrastructure, as well as in the dissemination of HPC best practices in academia and industry. https://inrastes.demokritos.gr/personnel/karozis-stelios/ (this opens in a new tab); skarozis@ipta.demokritos.gr (this opens in a new tab)

Mihalis A. Nicolaou is currently an Assistant Professor at the Computation-based Science and Technology Center at the Cyprus Institute. He received the B.Sc. degree from the University of Athens, Greece, and the M.Sc. and Ph.D. degrees from the Department of Computing, Imperial College London, U.K, where he was also postdoctoral researcher. His research interests lie in machine learning, computer vision, and signal processing, focusing on the analysis and interpretation of multimodal, high-dimensional data, in a wide range of interdisciplinary applications. He has published over 70 research articles and has received several best paper awards.http://mihalisan.cyi.ac.cy/ (this opens in a new tab); m.nicolaou@cyi.ac.cy (this opens in a new tab)

References

1. N. P. Bakas, A. Langousis, M. Nicolaou, and S. A. Chatzichristofis, “Gradient free stochastic training of anns, with local approximation in partitions,” Stochastic Environmental Research and Risk Assessment, 2023. [Online]. Available: https://link.springer.com/article/10.1007/s00477-023-02407-2 (this opens in a new tab)

2. N. P. Bakas, V. Plevris, A. Langousis, and S. A. Chatzichristofis, “Itso: A novel inverse transform sampling-based optimization algorithm for stochastic search,” Stochastic Environmental Research and Risk Assessment, vol. 36, no. 1, pp. 67–76, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s00477-021-02025-w (this opens in a new tab)

3. D. Sculley, J. Snoek, A. Wiltschko, and A. Rahimi, “Winner’s curse? on pace, progress, and empirical rigor,” in Sixth International Conference on Learning Representations Workshop, 2018. [Online]. Available: https://iclr.cc/Conferences/2018/Schedule?showEvent=406 (this opens in a new tab)

4. M. Hutson, “Artificial intelligence faces reproducibility crisis,” Science, vol. 359, no. 6377, pp. 725–726, 2018.

5. C. Belthangady and L. A. Royer, “Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,” Nature methods, p. 1, 2019.

6. N. P. Bakas, “Numerical Solution for the Extrapolation Problem of Analytic Functions,” Research, vol. 2019, pp. 1–10, may 2019. [Online]. Available: https://spj.sciencemag.org/research/2019/3903187/ (this opens in a new tab)