Machine Learning Approaches for Spatial Modeling of Agricultural Droughts
A special issue of AgriEngineering (ISSN 2624-7402).
Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 454
Special Issue Editors
Interests: drought modelling; machine learning; big data analysis; agricultural climate modelling; climate risk; applied climate science; communicating science
Interests: climate prediction; climate risk; applied statistics; agricultural-climate modelling; artificial intelligence; big data analysis; remote sensing and GIS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Drought is often considered as one of the most serious natural hazards causing a significant impact on a wide range of social, economic, and ecological systems. Climate change increases droughts recurrence, severity, duration, and spatial extent. Understanding and modelling drought and its impacts is of paramount importance for society and may serve as a critical mitigation practice for sustainable development. The aim of this Special Issue is to explore innovative technologies to forecast and monitor droughts at unprecedented levels of accuracy and resolution that can help to improve the profitability and productivity of agriculture during times of droughts.
In line with the context and aims outlined above, we invite original contributions on (but not limited to) the following topics:
- Spatial modelling of droughts;
- Machine learning approaches for prediction of agricultural (spatial) droughts;
- Big data analytics for drought modelling and prediction;
- Drought risk analysis using state-of-the-art techniques, variability and adaptation.
Dr. Kavina Dayal
Dr. Thong Nguyen-Huy
Guest Editors
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. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.
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Keywords
- Drought modelling and prediction
- Agricultural droughts
- Machine learning
- Spatial and temporal drought risks
- Big data analysis
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