Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Eslamian, Saeid; Maleki, Mousa; Nanjundan, Preethi Title A name given to the resource Multimodal data analytics for climate and water resources management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.265-277 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00021-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00021-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032944473?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032944473?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Eslamian S., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran; Maleki M., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran; Nanjundan P., Department of Data Science, CHRIST University, Lavasa Campus, Maharashtra, Pune, India Description An account of the resource The incorporation of multimodal data analytics into climate and water resource management has become a groundbreaking strategy for tackling intricate environmental issues. This chapter examines the importance of integrating various data sourcesincluding satellite imagery, weather sensors, textual reports, and social media feedsto develop a comprehensive perspective on climate and water systems. It addresses key challenges such as data heterogeneity, computational demands, and potential biases while showcasing the significant benefits of multimodal data in enhancing predictive modeling and decision-making. The discussion extends to advanced methodologies for data acquisition, integration, and feature extraction, with a focus on machine learning and deep learning techniques. Additionally, real-world applications in climate prediction, drought and flood forecasting, and water quality assessment are explored. The chapter also considers ethical concerns and future advancements in multimodal analytics, emphasizing the importance of responsible data utilization and innovative research to strengthen climate adaptation and water resource management efforts. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource climate prediction; data fusion; deep learning; Multimodal data; water resource management Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online