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Applications of Artificial Intelligence in Water Resources Forecasting

Golmar Golmohammadi, , , andNikolaos Tziolas


Release Date: 12/22/2025

DOI: 10.32473/edis-ss752-2025

Natural Resources

Abstract

The advent of artificial intelligence (AI) and machine learning has permeated every aspect of our lives. Water resource management is becoming increasingly important due to the impacts of climate change and human activities. This publication provides examples of how AI can potentially improve the forecasting of water resource variables at both the field and regional levels. The main goal is to introduce new AI techniques for forecasting key environmental factors (climate, soil, water) related to water resources in Florida. The results demonstrate how deep learning models performed consistently well and established their superiority in capturing the temporal dynamics of river discharge and groundwater in the region. The results also show a dramatic groundwater level drawdown in certain areas, which could jeopardize the ability to meet water demands for agricultural purposes. The increase in river discharge and high groundwater levels might be related to flooding in those areas.

About this Publication

This document is SL537, one of a series of the Department of Soil, Water, and Ecosystem Sciences, UF/IFAS Extension. Original publication date December 2025. Visit the EDIS website at https://edis.ifas.ufl.edu for the currently supported version of this publication. © 2025 UF/IFAS. This publication is licensed under CC BY-NC-ND 4.0.

How to Cite

Golmohammadi, G., Biazar, S. M., Nedhunuri, R. R., & Tziolas, N. (2025). Applications of Artificial Intelligence in Water Resources Forecasting. EDIS, 2025(6). https://doi.org/10.32473/edis-ss752-2025

Authors

Golmar Golmohammadi
Golmar Golmohammadi
Specialist
University of Florida

assistant professor, watershed hydrology, Department of Soil, Water, and Ecosystem Sciences, UF/IFAS Range Cattle Research and Education Center, Ona, FL

Seyed Mostafa Biazar
Seyed Mostafa Biazar

doctoral student, UF/IFAS Department of Soil, Water, and Ecosystem Sciences, Gainesville, FL

Rohith Reddy Nedhunuri
Rohith Reddy Nedhunuri

software development engineer, Amazon, and UF alum (M.S. 2024) and former software development engineer, UF Department of Computer and Information Science

Nikolaos Tziolas
Nikolaos Tziolas
Specialist
University of Florida

assistant professor, soil science artificial intelligence, Department of Soil, Water, and Ecosystem Sciences, UF/IFAS Southwest Research and Education Center, Immokalee, FL

Publication #SL537

Peer Reviewed
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