Statistical Methods for Hydrological and Environmental Prediction and Management
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3256
Special Issue Editors
Interests: hydrology; environment; climate; statistics; machine learning; hydrological modeling; water quality; limnology; education
Special Issue Information
Dear Colleagues,
This Special Issue invites papers that explore new and innovative uses of statistical methods to solve problems in hydrological and, more broadly, environmental systems. The application of statistical methods in hydrology is well established, as demonstrated by the availability of foundational texts, such as those by Beard (1962), Haan (1967), Clarke (1994), and Hipel and McLeod (1994). Since then, we have seen an explosion of research that applies statistical methods such as artificial neural networks and random forests applied to hydrological and environmental problems. Many of these methods are now accepted approaches and readily accessible via opensource statistical software packages such as R and Python. With this mass of development, the question is the following: have we reached the pinnacle of innovation? Therefore, this Special Issue invites authors to submit research that applies new statistical methods or shows innovative use of existing methods to solve current and emerging problems in hydrology and environmental management and that have the potential to kickstart a new wave of innovation.
Dr. Floris Van Ogtrop
Assoc. Prof. Dr. Rutger Willem Vervoort
Guest Editors
Manuscript Submission Information
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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
- statistical methods
- hydrology
- environment
- management
- uncertainty
- climate
- modeling
- computer
- data
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