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"AI/ML-driven framework"×
irrigation×

Quantifying Energy Use across U.S. Water Sectors: A Scalable AI/ML-Driven Framework for Data Integration and Resilience Planning

This white paper explores opportunities to quantify and characterize energy use across major U.S. water sectors, including irrigation, water supply, and water treatment, with a focus on energy for water dimension. A scalable AI/ML-driven framework will be developed to harmonize fr...
Ghimire, G., Bhanja, S., and Martinez, R.

Energy-Efficient Water Management Using Geo-AI for Evapotranspiration and Crop Risk Analysis

This white paper outlines a strategy for enhancing energy-efficient water management in agriculture through the application of Geospatial Artificial Intelligence (GeoAI). The focus is on developing a Dynamic Irrigation Window Scheduling (DIWS) system that optimizes irrigation base...
Cafferty, K., and Pacheco, C.

Aligning Energy, Water, and Agriculture: Near-Term Strategies for Resilience in Water-Stressed Regions

This white paper explores the intersection of energy, water, and agriculture, emphasizing the growing competition for water between irrigation and energy systems. It identifies near-term opportunities to enhance agricultural water management and electrification, while evaluating t...
Pacheco, C., and Mekonnen, M.

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