Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 12737
Special Issue Editors
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: geographic information system (GIS); remote sensing (RS); machine learning; disaster management; natural hazards; multiple-criteria decision making (MCDM); water quality, waste management
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; artificial intelligence; hydrology; climate change; environmental monitoring; water resources; groundwater
Interests: sustainable environment development; water resources management; hydrological modeling; artificial intelligence; time series analysis ;rainfall–runoff
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
(1) Introduction, including scientific background and highlighting the importance of this research area
The journal Water (ISSN: 2073-4441, IF 3.530) is currently running a Special Issue entitled “Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting”. Dr. Quoc Bao Pham, Dr. Sk Ajim Ali, Dr. Sani Isah Abba and Dr. Rana Muhammad Adnan Ikram are serving as Guest Editors for this Special Issue. We think you could make an exceptional contribution based on your expertise in this particular field.
Hydro-meteorological extremes have a broad spatial scope and a time sequence, and can be impacted by a variety of climatological and geographic factors. Making effective forecasts requires an understanding of the pertinent spatial and temporal information. The competences of artificial intelligence (AI) methods can be applied in such instances due to their enhanced abilities in learning complex relationships.
Recently, modelling and prediction of hydrological processes, climate change, and earth systems have benefited greatly from the use of AI technologies. Among them, machine learning (ML) and deep learning (DL) techniques are primarily cited as being necessary to improve model performance in terms of accuracy, robustness, efficiency, and computation cost. AI has the potential to lighten meteorologists' workloads, while increasing the precision of weather predictions. Scientists will have a better chance of warning people in danger, since AI technology can interpret data under harsh weather conditions quickly and accurately.
Considering these advantages of AI and machine/deep learning, the main objective of this Special Issue is to provide a scientific forum for advancing the successful application of artificial intelligence and machine/deep learning models toward hydro-meteorological forecasting and monitoring in various climate-related hazard-prone regions of the earth, as well as to foster informed discussions among scientists and stakeholders on this pressing issue.
(2) Aim of the Special Issue and how the subject relates to the journal scope
Scientists can benefit from the use of artificial intelligence systems, machine learning, neural networks, and deep learning while performing complex tasks such as weather forecasting. These technologies are very versatile and have been demonstrated to be more accurate than conventional methods at predicting weather patterns. Systems can be given a large amount of information, and after analysing the data they receive, they can learn to recognise natural phenomena, such as hurricanes, storms, snowfalls, and much more.
Thus, this Special Issue aims to provide an outlet for high-quality peer-reviewed publications that implement state-of-the-art models and techniques that incorporate AI- and ML-based methods to map, evaluate, and model hydro-meteorological forecasting, its monitoring, and their implications together, with the framing of newer hypotheses that can further our understanding of operative processes.
(3) Suggested themes and article types for submissions
The Special Issue may include (without being limited to) the following themes:
- Artificial intelligence in Hydro-meteorology;
- Data-driven approach for hydro-meteorological modelling;
- Machine and deep learning in micro-climate assessment;
- AI and machine learning for weather predictions;
- Spatio-temporal hydrological extremes through AI and machine learning;
- Machine learning for weather and climate;
- Monitoring hydrological hazards through remote sensing, GIS and machine learning;
- Artificial intelligence for disaster risk reduction;
- Potential of deep learning in multi-hazard assessment.
Given your competence in this area, we invite you to contribute a paper on the aforementioned subjects or any relevant issues.
Dr. Quoc Bao Pham
Dr. Sk Ajim Ali
Dr. Sani Isah Abba
Dr. Rana Muhammad Adnan Ikram
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Water is an international peer-reviewed open access semimonthly 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 2600 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.
Keywords
- artificial intelligence
- machine learning
- deep learning
- climate change forecasting
- hydro-meteorological analysis
- early warning system
- multi-hazard
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