Multimodal data analytics for climate and water resources management
- Title
- Multimodal data analytics for climate and water resources management
- Creator
- Eslamian, Saeid; Maleki, Mousa; Nanjundan, Preethi
- Description
- 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.
- Source
- Multimodal Learning Using Heterogeneous Data;pp.265-277
- Date
- 01-01-2025
- Publisher
- Elsevier
- Subject
- climate prediction; data fusion; deep learning; Multimodal data; water resource management
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 978-044327528-9; 978-044327529-6;
- Format
- online
- Language
- English
- Type
- Book chapter
Collection
Citation
Eslamian, Saeid; Maleki, Mousa; Nanjundan, Preethi, “Multimodal data analytics for climate and water resources management,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/24213.