by WenJun Zhang (Sun Yat-Sen University, China & International Academy of Ecology and Environmental Sciences)

Table of Contents (74k)
Preface (34k)
Chapter 1: Introduction (218k)

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed.

Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods. This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

• Artificial Neural Networks: Principles, Theories and Algorithms:
  • Feedforward Neural Networks
  • Linear Neural Networks
  • Radial Basis Function Neural Networks
  • BP Neural Network
  • Self-Organizing Neural Networks
  • Feedback Neural Networks
  • Design and Customization of Artificial Neural Networks
  • Learning Theory, Architecture Choice and Interpretability of Neural Networks
  • Mathematical Foundations of Artificial Neural Networks
  • Matlab Neural Network Toolkit
• Applications of Artificial Neural Networks in Ecology:
  • Dynamic Modeling of Survivor Process
  • Simulation of Plant Growth Process
  • Simulation of Food Intake Dynamics
  • Species Richness Estimation and Sampling Data Documentation
  • Modeling Arthropod Abundance from Plant Composition of Grassland Community
  • Pattern Recognition and Classification of Ecosystems and Functional Groups
  • Modeling Spatial Distribution of Arthropods
  • Risk Assessment of Species Invasion and Establishment
  • Prediction of Surface Ozone
  • Modeling Dispersion and Distribution of Oxide and Nitrate Pollutants
  • Modeling Terrestrial Biomass