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ARS Home » Southeast Area » Stoneville, Mississippi » Sustainable Water Management Research » Research » Research Project #439578

Research Project: Indicator Based Approach to Estimate Impact of Land-Use/Land Cover Change on Water Footprint Using Artificial Intelligent (AI) Methods

Location: Sustainable Water Management Research

Project Number: 6066-13000-006-016-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Aug 23, 2021
End Date: Aug 22, 2024

Develop an approach to understanding and estimating land-use and land cover changes and the impact on water movement and availability using artificial intelligence (AI) methods. The objectives of the project are: 1. Assess water footprints under difference scenarios of land-use and land cover. 2. Use AI methods to represent the water use patterns in the environments from Objective 1.

The approach is to develop water footprints over two regions (Mississippi and Florida) using the ISO 14046 method. After the water footprints have been developed, the land-use/land cover change that occurred during the past 40 years will be estimated using principal component analysis in two regions in Mississippi and Florida. Integration of the land-use and land cover changes into the water footprint analysis will allow for the estimation of the impact by the characterization methodology and indicators. This approach is justified because the indicators support representation of complexity of water use patterns in a changing environment. This approach helps examine change, vulnerability, and adaptation in ecosystems as co-evolution between the system and the environment rather than separating them. The use of artificial intelligence (AI) methods is supported by the complexity of the relationship between water footprint changes and land-use and land cover changes over time. Data mining techniques, such as support vector machines, can estimate the complexity of the indicators and allow for monitoring of trends in the water ecosystem. At the conclusion of this project an understanding and representation of the complex relationship between water footprints and land-use and land cover change will allow for application of AI to this complex problem which will serve a wide-range of economically disadvantaged communities.