Special Issue "Data-Driven Methods for Agricultural Water Management"
Deadline for manuscript submissions: closed (30 June 2019).
Interests: machine learning; data mining; computational intelligence; computer vision
Interests: statistical modeling; machine fault diagnosis; prognostics and health management; mechanical signal processing; statistical signal processing; digital/adaptive signal processing; data mining; non-destructive testing; system diagnostics; energy systems
Special Issues and Collections in MDPI journals
Interests: machine learning; metaheuristic algorithms
2 Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
Interests: machine learning; computational intelligence; renewable energy systems; complex systems
Various sensors have allowed the collection of large volumes of data related to agricultural water management. However, conventional approaches and strategies lack the ability to take advantage of such large amounts of data and, thus, advanced data-driven methods are highly desired to achieve smart agricultural water management. Emerging advances in computing technologies have boosted the application of data-driven methods in different domains, such as in healthcare, power supply, and energy management. Since data-driven methods can be employed for forecasting, classification, optimization, etc., it is valuable and meaningful to develop and apply data-driven methods to relevant aspects in the agricultural water management domain.
The aim of this Special Issue is to report on recent advances relating to the following themes: (1) data-driven methods for farm-level and regional water management; (2) data-driven methods for irrigation, drainage, and salinity in cultivated areas; (3) data-driven methods for rainwater harvesting and crop water management in rainfed areas; (4) data-driven methods for groundwater management in agriculture and conjunctive use of groundwater and surface water; and (5) data-driven methods for all related fields of agricultural water management.
Dr. Long Wang
Dr. Dong Wang
Dr. Shancheng Jiang
Dr. Chao Huang
Manuscript Submission Information
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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 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.
- data-driven methods
- agricultural water management