[Paper Review] A Review on the Application of Natural Computing in Environmental Informatics

Andreas Kamilaris|arXiv (Cornell University)|Aug 1, 2018

Water Quality Monitoring Technologies59 references3 citations

TL;DR

This paper reviews the application of natural computing techniques—such as evolutionary algorithms, neural networks, and swarm intelligence—in environmental informatics. It synthesizes existing research, evaluates the strengths and limitations of these methods, and highlights their role in modeling environmental complexity, with key contributions in methodological guidance and interdisciplinary integration for sustainability research.

ABSTRACT

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.

Motivation & Objective

• To examine the integration of natural computing in environmental informatics for modeling complex environmental systems.
• To identify and analyze nature-inspired computational techniques used in environmental research.
• To evaluate the advantages and limitations of these techniques in solving real-world environmental problems.
• To provide a comprehensive overview of current research trends and applications in the field.
• To guide future research by identifying gaps and opportunities in the application of natural computing to environmental challenges.

Proposed method

• Systematic review of peer-reviewed literature on natural computing applications in environmental informatics.
• Categorization of techniques into evolutionary computation, artificial neural networks, and swarm intelligence.
• Analysis of case studies and applications across environmental domains such as pollution modeling, climate change, and resource management.
• Evaluation of methodological strengths, including adaptability and robustness in handling non-linear systems.
• Comparison of computational performance, scalability, and interpretability across different natural computing paradigms.
• Synthesis of findings into a structured framework for future research and application.

Experimental results

Research questions

• RQ1 Which natural computing techniques are most commonly applied in environmental informatics?
• RQ2 How do these techniques address the complexity and uncertainty inherent in environmental systems?
• RQ3 What are the key advantages and limitations of using natural computing in environmental modeling and decision support?
• RQ4 How do different natural computing methods compare in terms of accuracy, efficiency, and scalability for environmental applications?
• RQ5 What are the emerging trends and future research directions in the integration of natural computing with environmental informatics?

Key findings

• Evolutionary algorithms and neural networks are widely used for modeling environmental phenomena such as air and water quality, climate patterns, and land use change.
• Swarm intelligence techniques show promise in optimizing environmental monitoring and resource allocation tasks.
• Natural computing methods excel in handling non-linear, dynamic, and uncertain environmental systems where traditional models may fail.
• Despite their advantages, challenges remain in interpretability, computational cost, and model validation across diverse environmental contexts.
• The integration of natural computing with environmental informatics enhances predictive accuracy and supports sustainable decision-making.
• There is a growing trend toward hybrid models combining multiple natural computing techniques for improved performance in complex environmental applications.