# Integrated Water Resource Management in the Segura Hydrographic Basin: An Artificial Intelligence Approach

arXiv: https://arxiv.org/abs/2411.13566 · HTML: https://mlsanity.com/paper/2411.13566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 38 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models](https://mlsanity.com/paper/2508.13057/) (2025)

## Abstract (arXiv)

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.