Using Machine Learning Methods for Agricultural Water Cycle Assessment

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 53

Special Issue Editor

Collage of Water Resource and Architectural Engineering, Northwest A&F University, Xianyang, China
Interests: machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the dual pressures of global climate change and population growth, the shortage of agricultural water resources is an issue of increasing severity. The traditional methods for evaluating agricultural water resources are no longer able to meet the needs of modern agricultural production. Machine learning can extract useful features and patterns from massive amounts of data to predict the supply and demand of agricultural water resources. On this basis, by analyzing crop growth data and environmental factors (such as temperature, rainfall, and soil moisture), the growth and water demand patterns of crops are predicted. This Special Issue aims to explore the application of machine learning in agricultural water resource assessment to improve the accuracy and efficiency of assessment, as well as provide theoretical support for the scientific management and efficient utilization of agricultural water resources.

Dr. Ze Liu
Guest Editor

Manuscript Submission Information

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Keywords

  • agricultural water resources
  • machine learning
  • water demand
  • big data
  • features

Published Papers

This special issue is now open for submission.
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