Machine Learning and Clustering for a Sustainable Future : Applications in Engineering and Environmental Science / by Alma Yunuen Raya-Tapia, Francisco Javier López-Flores, César Ramírez-Márquez, José María Ponce-Ortega

Von:

Raya-Tapia, Alma Yunuen [VerfasserIn]

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Studies in Computational Intelligence ; 1233Verlag: Cham : Springer Nature Switzerland, 2025Verlag: Cham : Imprint: Springer, 2025Auflage: 1st ed. 2025Beschreibung: 1 Online-Ressource(XIV, 351 p. 150 illus., 135 illus. in color.)ISBN:

9783032038760

Schlagwörter:

006.3 23

DOI: DOI: 10.1007/978-3-032-03876-0Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Chapter 1. Artificial Intelligence, Machine Learning, and Clustering in Sustainability -- Chapter 2. Fundamentals of Clustering: Methods, Metrics, and Optimization -- Chapter 3. Programming for Clustering: Python, R, MATLAB, and Anaconda Libraries -- Chapter 4. Clustering Applications in Process Systems Engineering -- Chapter 5. Greenhouse Gas Emissions Clustering for Net Zero Goals -- Chapter 7. The Energy-Food Nexus: Geopolitical and Health Crises Analysis -- Chapter 8: Clustering Urban Zones: A Study of Gentrification -- Chapter 10: Spatiotemporal Clustering of Dam Filling Patterns.Zusammenfassung: This book explores cutting-edge machine learning and clustering techniques to tackle critical challenges in engineering, environmental science, and sustainability. The book provides an in-depth examination of clustering methodologies, covering unsupervised and supervised techniques, data preprocessing, distance metrics, and cluster validation methods such as the elbow and silhouette techniques. Readers will find practical insights into applying these methods to real-world problems, including clustering greenhouse gas emissions, optimizing energy systems, and analyzing the energy-food nexus in the context of global crises. By integrating theoretical foundations with hands-on applications, this book serves as a valuable resource for researchers, engineers, and professionals seeking data-driven solutions for sustainability challenges.PPN: PPN: 193604885XPackage identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT

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