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Hydrological Insights: Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability

by Hossein Hashemi

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Hydrology through models, remote sensing, and AI

$203.28 USD
$203.28 USD $203.28 USD

Paperback New English

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AlbakiReads Editorial

Hydrology through models, remote sensing, and AI

Organized in six parts, this edited volume moves from the principles and methods of hydrological modeling to tools intended to improve the accuracy and efficiency of hydrological studies. It then brings remote sensing and artificial intelligence into the discussion, alongside environmental impact assessment and strategies for sustainable water-resource management. Later sections address remote sensing and machine learning in contemporary hydrology, before turning to case studies and future directions. The structure connects foundational material with practical applications, giving the book a broad scope across modeling, technology, and water sustainability.

Good fit for readers who enjoy

  • Students seeking an introduction to hydrological modeling and its applications
  • Researchers exploring the intersection of hydrology, remote sensing, and AI
  • Water and environmental professionals interested in resource management approaches

Themes

Hydrological modelingRemote sensing and artificial intelligenceMachine learning in hydrologyEnvironmental impact assessmentSustainable water-resource management

Based on publisher information and book metadata.

Book overview

Hydrological Insights: Synergizing Groundwater Models, Remote Sensing, and AI for Water Sustainability offers an in-depth exploration of hydrological modeling and its cutting-edge advancements, presented across six comprehensive sections. Part I establishes the foundational principles and methodologies of hydrological modeling, while Part II delves into sophisticated techniques and tools that enhance the accuracy and efficiency of hydrological studies. Part III highlights the powerful integration of remote sensing and artificial intelligence, showcasing how these technologies revolutionize modern hydrological practices.

Part IV focuses on environmental impact assessment and management strategies, outlining effective methods for sustainable water resource management. Part V covers the latest advancements in remote sensing and machine learning, emphasizing their pivotal role in contemporary hydrology. Finally, Part VI presents real-world case studies and future directions, offering practical insights and forward-looking perspectives. With meticulously crafted chapters that combine theoretical foundations with practical applications, this book is an essential resource for students, researchers, and professionals seeking to advance their understanding of hydrology through the integration of remote sensing and AI.
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