Información General

• Autores: Daniel Gutiérrez Reina (ed. lit.), Pablo Millan Gata (ed. lit.), Julián Blasco Moreno (ed. lit.)
• Editores: Springer Suiza
• Año de publicación: 2026
• País: Suiza
• Idioma: español
• ISBN: 9783032030498, 9783032030511
• Texto completo no disponible (Saber más ...)

Resumen

• This study identifies existing gaps in the sustainable management of drought by applying it to derived Hydrological Ecosystem Services (HES). The proposal integrates physical and socioeconomic indicators, assessing the impact of socioeconomic factors such as quality of life, economic development, communication networks, and demographic changes, tailored to regional characteristics to evaluate the impact of these factors on environmental change in the HES. Finally, the present work proposes a strategy to generate a comprehensive modeling for introducing practical indicators to process the evaluation of them for future research. The selection of drought analysis indices depends on available data and regional requirements, necessitating the integration of diverse data and advanced modeling. The research methodology is divided into three parts: theoretical, analytical, and developmental. The analytical part involves evaluating possible collected data, emphasizing statistical techniques and spatial monitoring tools. The developmental part focuses on scenario analysis and the evaluation of risks, mitigation, and adaptation strategies.

Otros catálogos

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Listado de artículos

• • Data science and public policies: towards water security

    Regina León Ovelar

    págs. 1-28

    

  • Sources and effects of water contamination: characteristics and ecological implications

    Carlos Morales Polo, María del Mar Cledera Castro

    págs. 29-57

    

  • Use of omics techniques for assessing water quality: omics to evaluate water quality

    José Alhama Carmona, Marina Barbudo Lunar, Carmen Michán Doña

    págs. 59-86

    

  • Hyperspectral technology to monitor marine pollution

    Ámbar Pérez García, Adrian Rodriguez Molina, Emma Hernández Suárez, Alba Martín Lorenzo, Jose Francisco López Feliciano

    págs. 87-125

    

  • Machine and deep learning approaches for water pollution detection using hyperspectral imaging

    María Gema Carrasco García, María Inmaculada Rodríguez García, Francisco Javier González Enrique, Juan Jesús Ruiz Aguilar, Ignacio José Turias Domínguez

    págs. 127-156

    

  • Intelligent real-time anomaly detection for optimization of water monitoring systems

    Adrián Bazán Muñoz, Guadalupe Ortiz Bellot, Alfonso García de Prado Fontela, Manuel Cano Crespo, Juan Boubeta Puig, Enrique Daneri Vías, Juan Manuel Mariscal Chavinet

    págs. 157-182

    

  • Smart sensors for water quality monitoring in aquaculture systems

    María del Valle Zurita Lozano, Ángela Écija Arenas, María de la Paz Aguilar Caballos

    págs. 183-209

    

  • Drought impacts on hydrological ecosystem services: indicators and methodological processes

    Nadia Falah, Jaime Solís Guzmán, Cristina Torrecillas Lozano, Madelyn Marrero Meléndez

    págs. 211-257

    

  • Miniaturised (bio)sensors in aquatic environmental monitoring

    Vanesa Román Pizarro, Ángela Écija Arenas, Juan Manuel Fernández Romero

    págs. 259-289

    

  • Autonomous Surface Vehicle for Water Monitoring Using Artificial Intelligence Methodologies

    Alejandro Casado Pérez, Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Dame Seck Diop, Luis Miguel Díaz, Manuel A. Perales Esteve, Sergio Luis Toral Marín, Daniel Gutiérrez Reina

    págs. 291-322

    

  • From concept to control: development of an advanced ASV platform for testing

    Manuel Eduardo Gantiva Osorio, Morel Otazu. Thalia Alicia, Guillermo Bejarano Pellicer, Pablo Millan Gata, Federico Peralta Samaniego

    págs. 323-374

    

  • Model-based online planning strategy for environmental disaster scenarios with autonomous vehicles

    Samuel Yanes Luis, Sergio Luis Toral Marín, Daniel Gutiérrez Reina

    págs. 375-404

    

  • Unmanned underactuated surface vehicle formation control using deep reinforcement learning

    Luciano Villarreal Álava, Federico Peralta Samaniego, Pablo Millan Gata, Guillermo Bejarano Pellicer

    págs. 405-431

    

  • Multitask multiagent deep reinforcement learning for a fleet of autonomous surface vehicles in environmental cleanup missions

    Dame Seck Diop, Samuel Yanes Luis, Manuel A. Perales Esteve, Daniel Gutiérrez Reina, Sergio Luis Toral Marín

    págs. 433-468

    

  • Deep reinforcement learning and informative path planning: diving into the cooperation of heterogeneous aquatic surface vehicles

    Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio Luis Toral Marín

    págs. 469-502