by Wenjun Zhang (Sun Yat-Sen University, China)

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.

• Part I:
  • Linear Neural Network and General Regression Neural Network
  • Probabilistic Neural Network
  • BP Neural Network
  • RBF Neural Network
  • Functional Link Artificial Neural Network
  • Wavelet Neural Network
  • Self-Organizing Neural Networks
  • Discrete and Continuous Hopfield Neural Networks
  • LVQ Neural Network
  • ELMAN Neural Network
  • Self-Designed Neural Network
  • Interpretability of Neural Networks
  • Mathematical Principles of Neural Networks
  • Matlab Neural Network Toolkit
• Part II:
  • Dynamic Modeling of Survivor Process
  • Simulation of Dynamics of Plant Growth
  • Spatial Distribution Modeling of Arthropods
  • Spatial Succession Modeling of Biological Communities
  • Species Richness Estimation
  • Pattern Classifications of Ecosystems and Functional Groups
  • Simulation of Food Intake Dynamics
  • Modeling Arthropod Abundance from Plant Composition
  • Prediction of Primary Production Levels
  • Pest Risk Assessment
  • Sediment Transfer Prediction
  • Prediction of Forest Characteristics
  • Surface Ozone Estimation
  • Prediction of Nitrogen Dioxide Dispersion