Data-Driven Environmental Intelligence

Siddhartha Bhattacharyya, Jan Platoš, Soumyadip Dhar, Naba Kumar Mondal, Ivan Zelinka, Jyoti Sekhar Banerjee, Abhijit Das

• Publisher: CRC Press
• Year: 2026
• eBook ISBN: 9781040691649
• Topic: Technology & Engineering
• Subtopic: Computer Engineering
• Index: Technology & Engineering

The text provides an extensive discussion on hybrid intelligent techniques and their variants for application to environmental data-centric systems, often guided by a batch process. This book reviews the fundamental concepts of gathering, processing, and analyzing data from batch processes, followed by a review of intelligent tools and techniques that can be used in this direction. The book will also cover novel intelligent algorithms for the purpose of effective environmental pollution data management at par with the existing standards.

This book:

• Introduces novel hybrid intelligent techniques needed to address environmental pollution for the well-being of the global environment
• Examines the latest hybrid intelligent technologies and algorithms related to state-of-the-art methodologies for monitoring and mitigating environmental pollution
• Introduces techniques for the removal of heavy metals, phenol, azo, and non-azo dyes from industrial effluents
• Explores green synthesis of nanofilters and their application to environmental data management
• Illustrates the statistical prediction of nanoparticle levels for controlling vector population and Internet of Things-enabled hybrid intelligent environment management

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electrical and communications engineering, computer science and engineering, and environmental engineering.

Table of contents

• Cover Page
• Half Title page
• Series Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• About the editors
• Contributors
• Chapter 1 Data-driven environmental intelligence: Unlocking the power of data for a sustainable planet
• Chapter 2 Hybrid computational intelligence techniques for environmental hazard prediction and risk management
• Chapter 3 Microorganism image clustering based on type-2 fuzzy sets and restricted equivalent functions
• Chapter 4 Intelligent light population management
• Chapter 5 Removal of heavy metals, phenol, azo, and non-azo dyes from industrial effluents
• Chapter 6 Green synthesis of nanoparticles and their applications in various fields: Analyzing the current status in 2025
• Chapter 7 Harnessing nature’s green filter: A comprehensive review of phytoremediation for airborne pollutants
• Chapter 8 Application of regression models in pollution monitoring: Insights and implications
• Chapter 9 Preference-leveled evaluation functions in fuzzy AI systems for assessing cell abnormalities in healthcare IoT environments
• Chapter 10 IoT-enabled hybrid intelligent environmental management
• Chapter 11 AI and data-driven technologies in renewable energy systems for environmental sustainability
• Chapter 12 Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress
• Chapter 13 Data-driven environmental intelligence: Concluding remarks and future directions
• Index