Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models

Dear Colleagues,

We are in the era of climate change phenomena that may possibly intensify the hydrological cycle. Additionally, increasing urban development causes growing water demands as well as higher exposure to water-related risks, such as floods and droughts. Therefore, fundamental changes in the surface water and groundwater regimes are seen in many regions around the world. Responding appropriately to the rapidly growing water demands and increasing risks necessitates the use of innovative and novel techniques for better water resource management. The main priority is to develop suitable methods and models in order to establish, simulate, and predict the optimal management and use of available water resources.

In recent years, appropriate models such as artificial intelligence (AI) and machine learning (ML) algorithms have been widely used in many research areas and applications but less applied in the fields of hydrology. In this regard, Atmosphere will publish a Special Issue on “Artificial Intelligence and Machine Learning; Application in Predictive Hydrological Models”. This Special Issue seeks high-quality contributions on practical applications of AI and ML methods and models in prediction, forecast, and projection of hydrological events. You are cordially invited to submit your research papers to this upcoming Special Issue. All elements relevant to predictive studies in hydrology and groundwater modelling using, but not limited to, one or more of the below listed methods are within the scope of this Special Issue: neural networks, support vector machines (SVM), fuzzy systems, ANFIS, evolutionary computation, Bayesian network, Markov model, Kalman Filter, and chaos theory.

Authors of articles that either combine these models and algorithms together or combine AI and ML with other data-driven/physical models (e.g., development of AI algorithms to complement or integrated with hydrological conceptual or process-based modeling) are also encouraged to submit.  

Dr. Mohammad Zare
Dr. Guy Schumann
Guest Editors

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. Atmosphere 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 2400 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.