Data-Driven Environmental Intelligence

Hybrid Intelligent Techniques for Environmental Data Management

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.

Key Features of the 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

Intended Audience

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.