Note: Front Cover -- Advanced Modelling Techniques Studying Global Changes in Environmental Sciences -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Introduction: Global changes and sustainable ecosystem management -- 1.1. Effects of Global Changes -- 1.2. Sustainable Ecosystem Management -- 1.3. Outline of This Book -- 1.3.1. Review of ecological models -- 1.3.2. Ecological network analysis and structurally dynamic models -- 1.3.3. Behavioral monitoring and species distribution models -- 1.3.4. Ecological risk assessment -- 1.3.5. Agriculture and forest ecosystems -- 1.3.6. Urban ecosystems -- 1.3.7. Estuary and marine ecosystems -- References -- Chapter 2: Toward a new generation of ecological modelling techniques: Review and bibliometrics -- 2.1. Introduction -- 2.2. Historical Development of Ecological Modelling -- 2.3. Bibliometric Analysis of Modelling Approaches -- 2.3.1. Data Sources and Analysis -- 2.3.2. Publication Output -- 2.3.3. Journal Distribution -- 2.3.4. Country/Territory Distribution and International Collaboration -- 2.3.5. Keyword Analysis -- 2.4. Brief Review of Modelling Techniques -- 2.4.1. Structurally Dynamic Model -- 2.4.2. Individual-Based Models -- 2.4.3. Support Vector Machine -- 2.4.4. Artificial Neural Networks -- 2.4.5. Tree-Based Model -- 2.4.6. Evolutionary Computation -- 2.4.7. Ordination and Classification Models -- 2.4.8. k-Nearest Neighbors -- 2.5. Future Perspectives of Ecological Modelling -- 2.5.1. Big Data Age: Data-Intensive Modelling -- 2.5.2. Hybrid Models -- 2.5.3. Model Sensitivities and Uncertainties -- References -- Chapter 3: System-wide measures in ecological network analysis -- 3.1. Introduction -- 3.2. Description of system-wide Measures -- 3.3. Ecosystem Models Used for Comparison -- 3.4. Methods -- 3.5. Observations and Discussion -- 3.5.1. Clusters of Structure-Based Measures. , 3.5.2. Clusters of Flow-Based Measures -- 3.5.3. Clusters of Storage-Based Measures -- References -- Chapter 4: Application of structurally dynamic models (SDMs) to determine impacts of climate changes -- 4.1. Introduction -- 4.2. Development of SDM -- 4.2.1. The Number of Feedbacks and Regulations Is Extremely High and Makes It Possible for the Living Organisms and Populatio -- 4.2.2. Ecosystems Show a High Degree of Heterogeneity in Space and in Time -- 4.2.3. Ecosystems and Their Biological Components, the Species, Evolve Steadily and over the Long-Term Toward Higher Complexi -- 4.3. Application of SDMs for the Assessment of Ecological Changes due to Climate Changes -- 4.4. Conclusions -- References -- Chapter 5: Modelling animal behavior to monitor effects of stressors -- 5.1. Introduction -- 5.2. Behavior Modelling: Dealing with Instantaneous or Whole Data Sets -- 5.2.1. Parameter Extraction and State Identification -- 5.2.2. Filtering and Intermittency -- 5.2.3. Statistics and Informatics -- 5.3. Higher Moments in Position Distribution -- 5.4. Identifying Behavioral States -- 5.5. Data Transformation and Filtering by Integration -- 5.6. Intermittency -- 5.7. Discussion and Conclusion -- Acknowledgment -- References -- Chapter 6: Species distribution models for sustainable ecosystem management -- 6.1. Introduction -- 6.2. Model Development Procedure -- 6.3. Selected Models: Characteristics and Examples -- 6.3.1. Decision Trees -- 6.3.1.1. General characteristics -- 6.3.1.2. Examples -- 6.3.1.3. Additional remarks -- 6.3.2. Generalised Linear Models -- 6.3.2.1. General characteristics -- 6.3.2.2. Examples -- 6.3.2.3. Additional remarks -- 6.3.3. Artificial Neural Networks -- 6.3.3.1. General characteristics -- 6.3.3.2. Examples -- 6.3.3.3. Additional remarks -- 6.3.4. Fuzzy Logic -- 6.3.4.1. General characteristics -- 6.3.4.2. Examples. , 6.3.4.3. Additional remarks -- 6.3.5. Bayesian Belief Networks -- 6.3.5.1. General characteristics -- 6.3.5.2. Examples -- 6.3.5.3. Additional remarks -- 6.3.6. Summary of Advantages and Drawbacks -- 6.4. Future Perspectives -- References -- Chapter 7: Ecosystem risk assessment modelling method for emerging pollutants -- 7.1. Review of Ecological Risk Assessment Model Methods -- 7.2. The Selected Model Method -- 7.3. Case Study: Application of AQUATOX Models for Ecosystem Risk Assessment of Polycyclic Aromatic Hydrocarbons in Lake Ecos -- 7.3.1. Application of Models -- 7.3.2. Models -- 7.3.2.1. AQUATOX model -- 7.3.2.2. Parameterization -- 7.3.2.2.1. Biomass and physiological parameters of organisms -- 7.3.2.2.2. Characteristics of Baiyangdian Lake -- 7.3.2.2.3. PAHs model parameters -- 7.3.2.2.4. Determining PAHs water contamination -- 7.3.2.2.5. Sensitivity analysis -- 7.3.3. Results of Model Application -- 7.3.3.1. Model calibration -- 7.3.3.2. Sensitivity analysis -- 7.3.3.3. PAHs risk estimation -- 7.3.4. Discussion on the Model Application -- 7.3.4.1. Compare experiment-derived NOEC with model NOEC for PAHs -- 7.3.4.2. Compare traditional method with model method for ecological risk assessment for PAHs -- 7.4. Perspectives -- Acknowledgments -- References -- Chapter 8: Development of species sensitivity distribution (SSD) models for setting up the management priority with water qua -- 8.1. Introduction -- 8.2. Methods -- 8.2.1. BMC Platform Development for SSD Models -- 8.2.1.1. BMC structure -- 8.2.1.2. BMC functions -- 8.2.1.2.1. Fitting SSD models -- 8.2.1.2.2. Determining the best fitting model based on DIC -- 8.2.1.2.3. Uncertainty analysis -- 8.2.1.2.4. Calculating the eco-risk indicator: PAF and msPAF -- 8.2.2. Framework for Determination of WQC and Screening of PCCs -- 8.2.2.1. WQCs calculation -- 8.2.2.2. PCCs screening. , 8.2.3. Overview of BTB Areas, Occurrence of PTSs, and Ecotoxicity Data Preprocessing -- 8.3. Results and Discussion -- 8.3.1. Evaluation of the BMC Platform -- 8.3.1.1. Selection of the best SSD models -- 8.3.1.2. Priority and posterior distribution of SSDs parameters -- 8.3.1.3. CI for uncertainty analysis -- 8.3.1.4. Validation of SSD models -- 8.3.2. Eco-risks with Uncertainty -- 8.3.2.1. Generic eco-risks for a specific substance -- 8.3.2.2. Joint eco-risk for multiple substances based on response addition -- 8.3.3. Evaluation of Various WQC Strategies -- 8.3.3.1. Abundance of toxicity data -- 8.3.3.2. Limitation of toxicity data -- 8.3.3.3. Lack of toxicity data -- 8.3.3.4. Implication for improvement of the local WQC in BTB -- 8.3.4. Ranking and Screening Using Various PCC Strategies -- 8.3.4.1. PNEC -- 8.3.4.2. Eco-risk calculated by BMC -- 8.3.4.3. EEC/PNEC -- 8.3.4.4. PCC list in BTB area -- 8.3.4.5. Implication for update of the local PCC list in BTB -- 8.4. Conclusion -- Acknowledgments -- References -- Chapter 9: Modelling mixed forest stands: Methodological challenges and approaches -- 9.1. Introduction -- 9.2. Review Methodology -- 9.2.1. Literature Review on Modelling Mixed Forest Stands -- 9.2.2. Ranking of Forest Models -- 9.3. Results and Discussion -- 9.3.1. Patterns of Ecological Model Use in Mixed Forests -- 9.3.2. Model Ranking -- 9.3.2.1. FORMIX -- 9.3.2.2. FORMIND -- 9.3.2.3. SILVA -- 9.3.2.4. FORECAST -- 9.3.3. Comparison of the Top-Ranked Models -- 9.4. Conclusions -- Acknowledgments -- References -- Chapter 10: Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences -- 10.1. Introduction -- 10.2. Approaches Based on Management Strategy Simulation -- 10.2.1. Simulation of Discrete Events in Agroecosystem Dynamics -- 10.2.2. Simulation of Agroecosystem Control. , 10.3. Design of Agroecosystem Management Strategy -- 10.3.1. Hierarchical Planning -- 10.3.1.1. HTN planning concepts -- 10.3.1.2. Planning approach in HTNs -- 10.3.1.3. Illustration based on the problem of selecting an operating mode in agriculture -- 10.3.2. Planning as Weighted Constraint Satisfaction -- 10.3.2.1. Constraint satisfaction problem -- 10.3.2.2. Networks of weighted constraints -- 10.3.2.3. Illustration based on crop allocation -- 10.3.3. Planning Under Uncertainty with Markov Decision Processes -- 10.3.3.1. Markov decision processes -- 10.3.3.2. Illustration using a forest management problem -- 10.4. Strategy Design by Simulation and Learning -- 10.5. Illustrations -- 10.5.1. SAFIHR: Modelling a Farming Agent -- 10.5.1.1. Decision problem -- 10.5.1.2. SAFIHR: Continuous planning -- 10.5.1.3. Overview of the overall operation -- 10.6. Conclusion -- References -- Chapter 11: Ecosystem services in relation to carbon cycle of Asansol-Durgapur urban system, India -- 11.1. Introduction -- 11.2. Methods -- 11.2.1. Study Area -- 11.2.2. Urban Forest -- 11.2.3. Agriculture -- 11.2.4. Anthropogenic Activities -- 11.2.5. Cattle Production -- 11.3. Analysis and Discussion -- 11.3.1. Ecosystem Services and Disservices of Urban Forest -- 11.3.2. Ecosystem Services and Disservices of Agricultural Field -- 11.3.3. Ecosystem Services and Disservices Through Anthropogenic Activities -- 11.3.4. Ecosystem Services and Disservices Through Cattle Production -- 11.3.5. Impact on Biodiversity -- 11.3.6. Cultural Services and Disservices -- 11.3.7. Future Perspective of Ecosystem Services -- 11.4. Conclusions -- Acknowledgments -- References -- Chapter 12: Modelling the effects of climate change in estuarine ecosystems with coupled hydrodynamic and biogeochemical mode -- 12.1. Introduction -- 12.2. Coupled Hydrodynamic and Biogeochemical Models. , 12.3. Models as Effective Tools to Support Estuarine Climate Change Impacts Assessment.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Intelligent Computing Systems -- 2 Semantic Tools -- Their Use for Knowledge Management in the Public Sector -- Abstract -- 1 Outlines -- 2 Introduction---Presentation of the Field of Interest -- 2.1 E-Government---The Opportunities Through the Semantic Web -- 2.2 Public Open Data for the Transition to `Open Government' -- 3 Related Work -- 4 Semantic Representation of Knowledge -- 4.1 The RDF Data Model -- 4.2 The URI's Use -- 4.3 RDF Schema Specification Language -- 4.4 Web Ontology Language---OWL -- 5 Reasoning Tools -- 5.1 SWRL Rules -- 5.2 The Query Language SQWRL -- 6 Presentation of Our Ontology Through Prot00E9g00E9 -- 6.1 The Ontology Development in Prot00E9g00E9 4.3 -- 6.2 The E-Government Ontology -- 6.2.1 Defining Classes -- 6.2.2 Defining Properties -- 6.3 The Use of RDF, RDFS, OWL and SWRL Through a Case Study -- 7 Data Mining Technology from Ontologies -- 7.1 SPARQL -- 7.2 SPARQL-DL in OWL2 Query Tab of Prot00E9g00E9 -- 7.3 DL Query Tool of Prot00E9g00E9 -- 8 Evaluation of Ontology -- 8.1 Categorization of the Ontology -- 8.2 Basic Principles of Design -- 8.3 Methodology of the Ontology Development -- 9 Conclusions -- References -- 3 From Game Theory to Complexity, Emergence and Agent-Based Modeling in World Politics -- Abstract -- 1 Introduction -- 2 Game Theory in World Politics -- 2.1 A Game Theoretic Approach of Global Environmental Diplomacy -- 3 From Game Theory to Complexity -- 3.1 Emergence in World Politics -- 4 Simulating Complexity with Agent-Based Modeling -- 4.1 Agent-Based Modeling Research in World Politics -- 4.1.1 Political Applications of ABM -- 5 Conclusions -- Acknowledgments -- References -- List of Software Resources -- 4 A Semantic Approach for Representing and Querying Business Processes -- Abstract -- 1 Introduction. , 2 Semantic Web Techniques in Management Information Systems -- 2.1 What's Worth in Combining Management Information Systems with Semantic Web Technologies? -- 2.2 Process Models, Conceptual Models and Ontologies -- 2.3 Querying Business Process Models -- 2.4 Related Work -- 3 A BPMN Semantic Process Model -- 3.1 The Research Methodology -- 3.2 Developing Business Process Models -- 3.3 Developing the Ontology -- 3.3.1 The Scope of the BPMN Elements -- 3.3.2 The Scope of the Generic BPMN Alternative Models -- 3.3.3 The Scope of the Agent or Actor Participating in the Process -- 3.4 Validating the Ontology -- 4 Querying Conventional Databases and Semantic Models -- 5 Conclusions -- References -- 5 Using Conversational Knowledge Management as a Lens for Virtual Collaboration in the Course of Small Group Activities -- Abstract -- 1 Introduction -- 2 Related Work and Motivation -- 2.1 Conversational Patterns -- 2.2 Design Frames and Technologies for CK Management -- 2.3 Consolidation and Research Focus -- 3 Methodology -- 3.1 Data Samples and Analysis -- 3.2 Language-Action Models -- 4 Implementation -- 4.1 Transformable Document Templates -- 4.2 The Portlets -- 5 Concluding Remarks -- Acknowledgment -- References -- 6 Spatial Environments for m-Learning: Review and Potentials -- Abstract -- 1 Introduction -- 2 List of Resources -- 3 Classification Criteria -- 4 Exemplary Environments -- 5 Comparison -- 6 Results -- 7 Conclusions/Future Work -- References -- 7 Science Teachers' Metaphors of Digital Technologies and Social Media in Pedagogy in Finland and in Greece -- Abstract -- 1 Introduction -- 2 Theoretical Background -- 2.1 Approaching Science -- 2.2 The Relationship Between Science and Digital Technology -- 3 The Study -- 3.1 Aims & -- Methods -- 3.2 The Context and the Participants -- 4 Findings -- 4.1 Science as Way of Thinking. , 4.2 Science as Method -- 5 Conclusions -- References -- 8 Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors -- Abstract -- 1 Introduction -- 2 Gaussian Process Kernel Machines -- 3 Methodology -- 4 Application to Monitoring Complex Energy Systems: The Boiling Water Reactor (BWR) Case -- 4.1 Problem Statement -- 4.2 Testing Results -- 5 Conclusions -- References -- 9 A Framework to Assess the Behavior and Performance of a City Towards Energy Optimization -- Abstract -- 1 Introduction -- 2 Policy Context -- 3 Current Relevant Initiatives -- 4 Description of the Framework -- 5 Municipal Building Level SCEAF -- 6 Conclusions -- Acknowledgment -- References -- 10 An Energy Management Platform for Smart Microgrids -- Abstract -- 1 Introduction -- 2 The Smart Polygeneration Microgrid Pilot Plant -- 3 The Energy Management Platform -- 4 The Supervisory, Control and Data Acquisition (SCADA) System -- 5 Results and Discussion -- 6 Conclusions and Future Research Lines -- References -- List of Resources -- 11 Transit Journaling and Traffic Sensitive Routing for a Mixed Mode Public Transportation System -- Abstract -- 1 Introduction -- 1.1 Limited Scope of Data -- 1.2 Formal Route Names Versus Informal Headsigns -- 1.3 Insufficient Stop Descriptions -- 1.4 Traffic Sensitivity in Routing/Trip Planning -- 2 Related Work -- 2.1 Crowdsourced Mapping and Real-time Tracking -- 2.2 Activity Detection -- 2.3 Trip Planning/Routing -- 2.3.1 Dijkstra's Algorithm -- 2.3.2 A* Search -- 2.3.3 Raptor -- 2.4 Trip Planning with Real-time Data -- 3 Methodology/Design -- 3.1 The Server/Back-End -- 3.1.1 GTFS Data Pre-processing -- 3.1.2 Server Design -- 3.1.3 The Modified RAPTOR Search Algorithm -- 3.2 The Mobile App -- 3.2.1 Search -- 3.2.2 Results/Journey Displays -- 3.2.3 Recording. , 3.2.4 Traffic Report -- 3.2.5 Results Display -- 3.2.6 Journey Display -- 3.2.7 Journal -- 3.2.8 Stop Editor -- 3.2.9 Route Editor -- 4 Tests and Results -- 4.1 Basic Routing Capacity -- 4.1.1 Survey -- 4.1.2 Demographics -- 4.1.3 Algorithm Evaluation -- 4.2 Traffic Sensitivity -- 4.3 Journey Recorder -- 5 Future Work -- 5.1 Base Estimate Correction -- 5.2 Preference-Weighing System -- 5.3 Traffic Flow Prediction -- 5.4 Further Evaluation of Mapping Ability -- 6 Conclusion -- References -- 12 Adaptation of Automatic Information Extraction Method for Environmental Heatmaps to U-Matrices of Self Organising Maps -- Abstract -- 1 Introduction -- 2 Problem Formulation -- 3 HInEx---Heatmap Information Extraction -- 3.1 The Idea -- 3.2 Heatmap Area Isolation -- 3.3 Clustering Image Pixels Based on Colors -- 3.4 Generating Tree Description -- 3.5 The Key Search and Its Analysis -- 3.6 The Axis Search and Their Analysis -- 3.7 Complete Heatmap Description -- 4 SOM Cluster Number Extraction Based on U-Matrix -- 4.1 The Idea of HInEx Application to SOM U-Matrix -- 4.2 Clustering -- 4.3 Extracting a U-Matrix Cell Corresponding to a Single Distance Between Neurons -- 4.4 Searching a Color Representing the Minimal Neuron Distance in SOM -- 4.5 Threshold-like Operation -- 4.6 Dilatation and Erosion-like Operations -- 4.7 Searching for the Number of Groups in SOM -- 5 SOM Generator Description -- 6 Experimental Study -- 7 Conclusion -- Acknowledgements -- References -- 13 Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization -- Abstract -- 1 Introduction -- 2 Evolutionary Optimization -- 3 Determination of the Pareto-Optimal Build Orientations in Stereolithography -- 3.1 Orientation Selection in SL -- 3.2 Algorithm Configuration and Implementation -- 3.3 Build Orientation Case Study. , 4 Determination of the Optimum Packing Layout in Stereolithography Machine Workspace -- 4.1 Optimization Scheme -- 4.2 Packing Layout Construction Process -- 4.3 Packing Layout Case Studies -- 5 Concluding Remarks -- References -- 14 Car-Like Mobile Robot Navigation: A Survey -- Abstract -- 1 Introduction -- 2 RRT-Based Methods -- 2.1 Unsafe Path Planning -- 2.2 Safe Path Planning -- 2.3 Rapidly Exploring Random Tree Algorithm on Rough Terrains (RRT-RT) -- 2.4 RRT Motion Planning Subsystem -- 2.5 Partial Motion Planning -- 2.6 Sensor-Based Random Tree (SRT) -- 2.7 RRT* Algorithm -- 2.8 Voronoi Fast Marching (VFM) and Fast Marching (FM2) -- 2.9 SBL Algorithm -- 2.10 Single-Query Motion Planning -- 2.11 Dynamic-Domain RRT -- 2.12 Transition-Based RRT -- 2.13 Parallelizing Rapidly-Exploring Random Tree (RRT) Algorithm on Large-Scale Distributed-Memory Architectures -- 2.14 Obstacle Sensitive Cost Function for Navigating Car-Like Robots -- 3 Methods Based on Fuzzy Logic -- 3.1 Distributed Active-Vision Network-Space System -- 3.2 Internet-Based Smart Space Navigation Using Fuzzy-Neural Adaptive Control -- 4 Sensor-Based Methods -- 4.1 Dynamic Window Approach (DWA) -- 4.2 Generalized Voronoi Graph (GVG) Theory -- 4.3 Navigation in Dynamic Environments Using Trajectory Deformation -- 4.4 Probabilistic Velocity Obstacle (PVO) -- 5 SLAM-Based Methods -- 5.1 On-line Path Following -- 5.2 The CyCab: A Car-Like Robot Navigating Autonomously and Safely Among Pedestrians -- 5.3 V-Slam -- 5.4 SLAM-Based Turning Strategy in Restricted Environments -- 5.5 L-Slam -- 6 Conclusions and Future Work -- 6.1 Future Directions in Autonomous Robot Navigation and Obstacle Perception -- 6.2 Future Directions in Applications of Autonomously-Navigating Robots -- References -- 15 Computing a Similarity Coefficient for Mining Massive Data Sets -- Abstract -- 1 Introduction. , 2 Related Work.