Computational Ecology: Artificial Neural Networks and Their Applications

Description

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. Cover Front Matter Fundamentals of Network Biology Copyright Preface About the Author Acknowledgments Contents Part 1: Mathematical Fundamentals 1 Fundamentals of Graph Theory 2 Graph Algorithms 3 Fundamentals of Network Theory 4 Other Fundamentals Part 2: Crucial Nodes/Subnetworks/ Modules, Network Types, and Structural Comparison 5 Identification of Crucial Nodes and Subnetworks/Modules 6 Detection of Network Types 7 Comparison of Network Structure Part 3: Network Dynamics, Evolution, Simulation, and Control 8 Network Dynamics 9 Network Robustness and Sensitivity Analysis 10 Network Control 11 Network Evolution 12 Cellular Automata 13 Self-Organization 14 Agent-based Modeling Part 4: Flow Analysis 15 Flow/Flux Analysis Part 5: Link and Node Prediction 16 Link Prediction: Sampling-based Methods 17 Link Prediction: Structure- and Perturbation-based Methods 18 Link Prediction: Node-Similarity-based Methods 19 Node Prediction Part 6: Network Construction 20 Construction of Biological Networks Part 7: Pharmacological and Toxicological Networks 21 Network Pharmacology and Toxicology Part 8: Ecological Networks 22 Food Webs Part 9: Microscopic Networks 23 Molecular and Cellular Networks Part 10: Social Networks 24 Social Network Analysis Part 11: Software 25 Software for Network Analysis Part 12: Big Data Analytics 26 Big Data Analytics for Network Biology References Index