Time Series Forecasting in Physical Geography
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 3978
Special Issue Editors
Interests: surface water hydrology; snow hydrology; remote sensing; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Interests: hydrology; environment; machine learning; remote sensing; hydroinformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last several years, time series forecasting via machine learning-based models, statistical-based models, and physically-based models has been rapidly providing solutions to many outstanding problems in the field of physical geography. In the field of physical geography, the artificial intelligence-based solution approach has indisputable advantages, and researchers have also been trying to solve environmental problems via the application of new technologies in time series forecasting. There are some linear and non-linear relationships in physical geography components (e.g., the water cycle) that can be simulated by observing symmetry and finding relationships between geographic variables. Due to the complex nature of physical geographic variables, it is important to consider symmetry in the time series forecasting of these variables. Time series forecasting via new technologies (machine learning, remote sensing and hybrid artificial intelligence-based models) could be widely used in different areas of physical geography, such as vegetation studies, drought monitoring and forecasting, rainfall-runoff modeling, groundwater studies, forest management, land cover studies, evaporation, and evapotranspiration forecasting, streamflow modeling, solar radiation simulation, precipitation prediction, and soil moisture modeling, etc. This Special Issue on "Time Series Forecasting in Physical Geography" is seeking original research papers about the applications of new technologies for time series forecasting in physical geography. Potential topics that will be covered by this Special Issue include, but are not limited to, the following:
- Using machine learning models in hydrological studies.
- Time series forecasting for symmetric exclusion.
- Deep learning for analyzing symmetries in physical geography.
- Time series forecasting by statistical-based models in physical geography.
- Application of artificial intelligence and remote sensing for time series prediction.
- Earth observation via satellite imagery.
- Symmetry and its role in physical geography.
Dr. Babak Mohammadi
Prof. Dr. Mohammed Achite
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. Symmetry 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.
Keywords
- physical geography
- hydrology
- time series forecasting
- artificial intelligence
- geographic information systems
- satellite image analysis
- evolutionary computation
- earth observation
- water resources management
