Traditional Statistics vs. Modern Machine Learning Approaches in Hydrology

Dear Colleagues,

We are pleased to announce the launching of a new Special Issue: "Traditional Statistics vs. Modern Machine Learning Approaches in Hydrology". We are looking forward to receiving your research papers or/and case studies.

The effectiveness of machine learning (ML), and more recently deep learning, in hydrological applications has been proven by many researchers. The first applications of ML in hydrology appeared almost 30 years ago and, despite their simplicity, were fairly efficient, though they fell short of the performance of the standard models. Since then, things have evolved, and nowadays, many studies regarding ML applications in engineering hydrology suggest that ML can not only outperform hydrological models but can also learn catchment similarities, which corresponds to a prior hydrological understanding. While the effectiveness of ML in engineering hydrology is not questioned, there is no strong evidence that ML methods can dominate the traditional approaches in statistical hydrology. Indeed, some researchers have found that stochastic and machine learning methods do not differ that dramatically in forecasting hydrological processes such as river discharge. This finding has some implications since the CPU intensity of the ML methods is a significant handicap. Another example is the work by researchers who consider the heuristic segmentation approach to be unparalleled in applications such as the construction of rating curves, double mass analysis, and time-shift detection, etc. Yet, the equivalent ML methods (unsupervised learning), in contrast to the heuristic methods, come with plenty of readily available tools and frameworks that require only minimal configuration. The scope of this Special Issue involves the comparative use of traditional and ML approaches in applications of statistical hydrology. To shed more light, this comparison should be comprehensive, taking into consideration not only the performance per se, but also the preparation stages. More specifically, the evaluation should take into account and discuss the theoretical background, the labour required to configure the model, the CPU time required to set up the model, and finally, its overall appeal to practitioners.

Dr. Evangelos Rozos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.