Skip to main content
IFAS ExtensionUniversity of Florida
Login
Faculty Member Image

Nikolaos Tziolas

Specialist

Send Email

Nikolaos Tziolas

Orcid ID0000-0002-1502-3219

Specialist

SWES profile page

UF Research profile

SWFREC profile

Publications

thumbnail for publication: Applications of Artificial Intelligence in Water Resources Forecasting

Applications of Artificial Intelligence in Water Resources Forecasting

Golmar Golmohammadi, Seyed Mostafa Biazar, Rohith Reddy Nedhunuri, andNikolaos Tziolas

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.

thumbnail for publication: Using AI Chat and Satellite Maps for Farm Decisions: A Practical Evaluation of Available Tools

Using AI Chat and Satellite Maps for Farm Decisions: A Practical Evaluation of Available Tools

Melpomeni Zoka, Tad Trimarco, andNikolaos Tziolas

Earth observation satellite data can provide valuable, field-scale information to support growers’ management decisions involving crop conditions, soil organic carbon, and water stress. Despite the rapid growth of these data and the geospatial platforms to access them, many farmers and land managers still struggle to use these resources in daily decision-making. This information is commonly presented in complex formats that non-expert users cannot easily translate into clear, confident, and timely decisions. Geospatial artificial intelligence (GeoAI) agents may offer a promising solution. Imagine asking a question and instantly getting a map-based answer with a short explanation and recommendation. This publication introduces the emerging concept of conversational GeoAI platforms, demonstrates what current tools can and cannot do, outlines key challenges, and discusses how these technologies may support future agricultural decision-making for farmers, consultants, and Extension agents.