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

• URL: http://arxiv.org/abs/2411.13566v1
• Date: Mon, 11 Nov 2024 10:35:41 GMT
• Title: Integrated Water Resource Management in the Segura Hydrographic Basin: An Artificial Intelligence Approach
• Authors: Urtzi Otamendi, Mikel Maiza, Igor G. Olaizola, Basilio Sierra, Markel Flores, Marco Quartulli,
• Abstract summary: This paper presents a paradigmatic framework for addressing these issues in water management scenarios. The proposed approach accurately predicts water availability, estimates demand, and optimize resource allocation on both short- and long-term basis. The methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.
• Score: 2.4189718223541044
• License:
• Abstract: 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 ($hm^3$) 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.

Related papers

• Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
  This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
  arXiv  Detail & Related papers  (2024-10-08T10:35:32Z)
• Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring [3.2266662249755025]
  It is proposed to use Local Gaussian Processes and Deep Reinforcement Learning to jointly obtain effective monitoring policies. A Deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model. Using a Double Deep Q-Learning algorithm, agents are trained to minimize the estimation error in a safe manner.
  arXiv  Detail & Related papers  (2024-01-09T15:58:15Z)
• A Novel Data-driven Numerical Method for Hydrological Modeling of Water Infiltration in Porous Media [0.4028503203417233]
  Rootzone soil moisture monitoring is essential for sensor-based smart irrigation and agricultural drought prevention. We present a novel data-driven algorithm named the DRW (Data-driven global Random Walk) algorithm.
  arXiv  Detail & Related papers  (2023-10-04T13:33:37Z)
• Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning [14.376630486051795]
  The proposed approach is applied to Lake Mead and Lake Powell in the Colorado River Basin. It produces 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%.
  arXiv  Detail & Related papers  (2023-07-11T10:38:31Z)
• Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling [0.20999222360659603]
  Agricultural sector faces significant challenges in water resource conservation and crop yield optimization. Traditional irrigation scheduling methods often prove inadequate in meeting the needs of large-scale irrigation systems. This paper proposes a predictive irrigation scheduler that leverages the three paradigms of machine learning to optimize irrigation schedules.
  arXiv  Detail & Related papers  (2023-06-14T19:38:44Z)
• CCE: Sample Efficient Sparse Reward Policy Learning for Robotic Navigation via Confidence-Controlled Exploration [72.24964965882783]
  Confidence-Controlled Exploration (CCE) is designed to enhance the training sample efficiency of reinforcement learning algorithms for sparse reward settings such as robot navigation. CCE is based on a novel relationship we provide between gradient estimation and policy entropy. We demonstrate through simulated and real-world experiments that CCE outperforms conventional methods that employ constant trajectory lengths and entropy regularization.
  arXiv  Detail & Related papers  (2023-06-09T18:45:15Z)
• AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning [0.0]
  The preservation, monitoring, and control of water resources has been a major challenge in recent decades. This paper proposes a water monitoring system using autonomous surface vehicles, equipped with water quality sensors.
  arXiv  Detail & Related papers  (2022-11-28T10:56:12Z)
• Applications of physics-informed scientific machine learning in subsurface science: A survey [64.0476282000118]
  Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation. The responsible use and exploration of geosystems are thus critical to the geosystem governance, which in turn depends on the efficient monitoring, risk assessment, and decision support tools for practical implementation. Fast advances in machine learning algorithms and novel sensing technologies in recent years have presented new opportunities for the subsurface research community to improve the efficacy and transparency of geosystem governance.
  arXiv  Detail & Related papers  (2021-04-10T13:40:22Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis [55.41644538483948]
  We study a statistical and machine learning framework for the prediction of water pipe failures. We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
  arXiv  Detail & Related papers  (2020-07-02T19:08:36Z)
• Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation [49.69139684065241]
  Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
  arXiv  Detail & Related papers  (2020-04-02T17:04:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.