Scholarlink

Efficient water management is essential in regions facing persistent imbalances between water supply and demand. The Segura Hydrographic Confederation has developed a digital twin platform integrating Artificial Intelligence (AI) tools for efficient water resource management. This study presents an AI-based water quality monitoring module integrated in this platform, that explores three modeling approaches: ML-based water quality predictions, DL-based forecasting, and surrogate modeling. The study demonstrates that AI-enhanced water quality monitoring can support decision-making processes.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-Based Water Quality Monitoring Module for the Segura Hydrographic Confederation Platform

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Izar Azpiroz,
  • David Velásquez,
  • Breno da Costa Paulo,
  • Iker Landa del Barrio,
  • Juan Odriozola,
  • Mikel Maiza

摘要

Efficient water management is essential in regions facing persistent imbalances between water supply and demand. The Segura Hydrographic Confederation has developed a digital twin platform integrating Artificial Intelligence (AI) tools for efficient water resource management. This study presents an AI-based water quality monitoring module integrated in this platform, that explores three modeling approaches: ML-based water quality predictions, DL-based forecasting, and surrogate modeling. The study demonstrates that AI-enhanced water quality monitoring can support decision-making processes.