Modeling and Simulation of Environmental Systems: A Computation Approach

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Acknowledgements
Editors
Contributors
Part I: Water
	Chapter 1: Computational Models for Water Resource Management: Opportunities and Challenges
		1.1 Introduction
		1.2 Mathematical Modeling for WRM
		1.3 Computational Modeling for WRM
			1.3.1 Numerical Models
			1.3.2 Development of Computational Model for WRM
			1.3.3 Mathematical Modeling of Flow and Transport in Groundwater
			1.3.4 Numerical Model – Finite Difference Method
			1.3.5 Numerical Model – Finite Element Method
			1.3.6 Numerical Model – Meshfree Radial Point Collocation Method
		1.4 Computational Models for WRM
			1.4.1 Models for Surface Water Management
			1.4.2 Models for Groundwater Management
		1.5 Case Studies
			1.5.1 Case Study 1
			1.5.2 Case Study 2
		1.6 Opportunities and Challenges in Computational Modeling for WRM
		1.7 Concluding Remarks
		References
	Chapter 2: Applicability of Soft Computational Models for Integrated Water Resource Management
		2.1 Introduction
		2.2 Overview of Soft Computing Methods
			2.2.1 Artificial Neural Network (ANN)
			2.2.2 Fuzzy Logic
			2.2.3 Genetic Algorithms
			2.2.4 Support Vector Machine (SVM)
			2.2.5 Hybrid Models
				2.2.5.1 Wavelet Based Hybrid Models
		2.3 Applications of Soft Computing Tools for IWRM
			2.3.1 Rainfall Runoff Modeling
			2.3.2 Statistical Downscaling of Meteorological Observations
			2.3.3 Water Quality Management
			2.3.4 Drought Assessment
			2.3.5 Ground Water Modeling
		2.4 Conclusions
		References
	Chapter 3: Computational Models for Exchange of Water between Ground Water and Surface Water Resources over a Sub-Basin
		3.1 Introduction
		3.2 Flow Processes
			3.2.1 Surface Runoff
				3.2.1.1 Governing Equations
			3.2.2 Infiltration
				3.2.2.1 Governing Equations
				3.2.2.2 Boundary Conditions
			3.2.3 Base Flow
			3.2.4 Ground Water–Surface Water (GW–SW) Interactions
		3.3 Computational Models
			3.3.1 Methodological Framework
			3.3.2 Data Requirement
			3.3.3 Classification of GW–SW Models
				3.3.3.1 Deterministic and Stochastic Models
				3.3.3.2 Coupled Models
				3.3.3.3 Fully Coupled Models
				3.3.3.4 Loosely Couple Models
			3.3.4 Challenges and Opportunities
		3.4 Applications of Computational Models in Impact Assessment
		3.5 Recent Trends in Modeling Techniques
		3.6 Summary
		References
	Chapter 4: Computational and Field Approach to Assess Artificial Recharge of Groundwater
		4.1 Introduction
		4.2 Study Area
			4.2.1 Artificial Recharge Structures
		4.3 Methodology
			4.3.1 Water Level Fluctuations
			4.3.2 Mass Balance Study
			4.3.3 Numerical Groundwater Modeling
				4.3.3.1 Flow Model (MODFLOW)
		4.4 Results and Discussions
			4.4.1 Water Level Fluctuations
			4.4.2 Mass Balance
				4.4.2.1 Percolation Pond
				4.4.2.2 Check Dams
			4.4.3 Numerical Groundwater Modeling
				4.4.3.1 Flow Model
				4.4.3.2 Model Construction
				4.4.3.3 Initial Conditions, Boundary Conditions, and Stresses
				4.4.3.4 Model Calibration and Validation
				4.4.3.5 Performance Evaluation of Individual Structures
		4.5 Conclusions
		References
	Chapter 5: Multi-Objective Optimization in Water Resource Management
		5.1 Introduction
		5.2 Mathematical Optimization
			5.2.1 Linear Programming Model
			5.2.2 Simple Multi-Objective Model
			5.2.3 Global Criteria Method
			5.2.4 Posteriori Method (Weighing Method)
		5.3 Steps to Implement Posteriori Technique
			5.3.1 Problem Definition
			5.3.2 Objective Functions
				5.3.2.1 Maximization of Revenue
				5.3.2.2 Minimization of Overutilization
				5.3.2.3 Minimization of Cost
				5.3.2.4 Minimization of Pollution
				5.3.2.5 Maximization of Treatment Plants
				5.3.2.6 Minimize Waste Generation
			5.3.3 Constraints
				5.3.3.1 Supply Constraint
				5.3.3.2 Demand Constraint
				5.3.3.3 Future Needs
				5.3.3.4 Budget Constraint
				5.3.3.5 Max. No. of CEP Plants
		5.4 Results
			5.4.1 Final Model Formulation
			5.4.2 Case Study Based on the Allocation of Water in State Delhi with the Available Data
		5.5 Conclusions
		5.6 Future Scope
		Acknowledgements
		References
	Chapter 6: Tools in Decision-Making of Allocation of Non-Traditional Resources for Sustainable Water Development
		6.1 Introduction
		6.2 Study Area
		6.3 Methodology
			6.3.1 Intrinsic Groundwater Vulnerability (Standard DRASTIC)
			6.3.2 Sensitive Analysis
			6.3.3 Modified DRASTIC (DRASTICQ)
		6.4 Results and Discussion
			6.4.1 Intrinsic Groundwater Vulnerability (Standard DRASTIC)
			6.4.2 Sensitivity Analysis
			6.4.3 Modified DRASTIC (DRASTICQ)
			6.4.4 Validation
		6.5 Discussion
		6.6 Conclusion
		References
	Chapter 7: Soft Computing Techniques for Forecasting of Water Demand
		7.1 Introduction
		7.2 Background
			7.2.1 Water Demand
			7.2.2 Factors Affecting Water Demand
			7.2.3 Preprocessing of Dataset
			7.2.4 Forecasting Horizons
		7.3 Forecasting Methods
			7.3.1 Statistical Approaches
			7.3.2 Soft Computing Approaches
				7.3.2.1 Artificial Neural Networks (ANN)
				7.3.2.2 Support Vector Machine (SVM)
				7.3.2.3 Metaheuristic Models
		7.4 Assessment of Forecasting Models
		7.5 Soft Computing Methodologies
			7.5.1 Artificial Neural Network (ANN)
			7.5.2 Long Short Term Memory (LSTM)
			7.5.3 Fuzzy Logic
			7.5.4 Adaptive Neuro-Fuzzy Interface System (ANFIS Model)
		7.6 Application of Models
			7.6.1 Spanish Dataset
		7.7 Results and Discussion
		7.8 Summary and Conclusions
		References
	Chapter 8: Intervention of Computational Models for Groundwater Pollution Source Characterization
		8.1 Introduction
			8.1.1 Background
			8.1.2 Groundwater Pollution Sources Characterization
		8.2 Methods for Characterization of Groundwater Pollutant Sources
			8.2.1 Direct Inverse Approach for Pollutant Source Characterization
			8.2.2 Statistical and Regression Methods for Pollutant Source Characterization
			8.2.3 Surrogate Model Based Approach
			8.2.4 Linked Simulation Optimization (LSO)
			8.2.5 Hybrid Techniques
		8.3 Mathematical Framework for USC
			8.3.1 Monitoring Well Design for Obtaining Concentration Data
			8.3.2 LSO formulation
			8.3.3 Result of USC using LSO
		8.4 Conclusions
		References
Part II: Air Pollution
	Chapter 9: Artificial Intelligence for Air Quality and Control Systems: Status and Future Trends
		9.1 Introduction
		9.2 Air Quality and Control Systems: Current Status of Pollution Research
			9.2.1 Background
			9.2.2 Initiatives for Air Quality Management
			9.2.3 Regulatory Framework for Air Quality Management and Forecasting
		9.3 Abbreviation Explanation and Error Assessment Index
		9.4 Future Trends Potential Forecasting Methods
			9.4.1 Air Pollution Forecasting and Analysis
			9.4.2 Some Implemented Systems for Air Quality Monitoring and Control
				9.4.2.1 Platform Screen Doors
				9.4.2.2 Wireless Sensor Network for Air Quality Monitoring
				9.4.2.3 Sensor-Based Wireless Air Quality Monitoring Network-SWAQMN (Polludrone)
				9.4.2.4 Some Other Applications of AI Environmental Sector
		9.5 Conclusion
		Acknowledgements
		Conflicts of Interest
		References
	Chapter 10: Fuzzy and Neural Network Model-Based Environmental Quality Monitoring System: Past, Present, and Future
		10.1 Introduction
		10.2 Scenario and Problems
		10.3 Air Pollution Modeling with Fuzzy and Neural Network Model
		10.4 Analysis of Available Soft Computing Models (Comparison of Methodology for Air Pollution Modeling)
			10.4.1 Multiple Linear Regression Models
			10.4.2 Artificial Neural Network Models
			10.4.3 Support Vector Machine Models
			10.4.4 Back Propagation Neural Network Models
			10.4.5 Fuzzy Logic and Neuro-Fuzzy Models
			10.4.6 Deep Learning Models
				10.4.6.1 Air Pollution Modeling with Deep Learning
		10.5 Ensemble and Hybrid Models
		10.6 Potential Soft Computing Models and Approaches
			10.6.1 Evolutionary Fuzzy and Neuro-Fuzzy Models
			10.6.2 Variations of ANN ModelsCase-Based Reasoning and Knowledge-Based Models
			10.6.3 Group Method Data Handling Models and Functional Network Models
			10.6.4 Appropriate Input Selection Methods
		10.7 Conclusions
		Acknowledgements
		Conflicts of Interest
		References
Part III: Internet of Things and Environmental Systems
	Chapter 11: Internet of Things (IoT): Powered Enhancements to Industrial Air Pollution Monitoring Systems
		11.1 Introduction
		11.2 Literature Survey
			11.2.1 Air Pollution
				11.2.1.1 Gaseous Pollutants
			11.2.2 IoT Applications in Air Pollution Monitoring
		11.3 Discussion
		11.4 Proposed Framework
		11.5 Future Research
		11.6 Conclusion
		References
	Chapter 12: Impact of Temporary COVID-Related Lockdowns on Air Quality across the Globe: A Systematic Review
		12.1 Introduction
		12.2 Periods of Temporary Lockdown(s) in the Major Countries across the Globe
		12.3 Methodology/Framework Adopted in Previous Studies
		12.4 Variation in the Air Quality Globally during Pre-Lockdown and Lockdown Scenarios in the Major Countries across the Globe
			12.4.1 United Kingdom (UK)
			12.4.2 India
			12.4.3 Italy
			12.4.4 Spain
			12.4.5 France
			12.4.6 China
		12.5 Discussion and Recommendations
		12.6 Conclusion
		References
	Chapter 13: Impact of Lockdown on Air Quality during COVID-19 Outbreak: A Global Scenario
		13.1 Introduction
			13.1.1 Global Overview of COVID-19 Pandemic
		13.2 Air Pollution Modeling
			13.2.1 Different Models Used for Analysis Using Computational Approach
			13.2.2 Recent Modeling Techniques and Trends in Statistical Modeling Tools
		13.3 Methodology for Air Quality Index
			13.3.1 Air Quality Indices and AQI Model for India
		13.4 Results: Impact of Lockdown on Global Air Quality
			13.4.1 Impact of Lockdown on Air Quality of Asian Countries
				13.4.1.1 Impact of Lockdown on Air Quality of India
				13.4.1.2 Impact of Lockdown on Air Quality of China
			13.4.2 Impact of Lockdown on Air Quality in United States of America (USA)
			13.4.3 Proposed Framework for the Air Pollution Monitoring and Modeling
		13.5 Summary of Global Air Quality during COVID-19 Lockdown
		13.6 Conclusion
		Acknowledgements
		References
	Chapter 14: Integration of Geospatial Techniques in Environment Monitoring Systems
		14.1 Introduction
		14.2 Environment Monitoring Systems
		14.3 Overview of Geospatial Techniques
			14.3.1 Remote Sensing Techniques
				14.3.1.1 Basics of Remote Sensing
				14.3.1.2 Remote Sensing Datasets
				14.3.1.3 Use of Remote Sensing for Environmental Monitoring
			14.3.2 Geographic Information Systems (GIS)
				14.3.2.1 Basics of GIS
				14.3.2.2 Recent Advances in GIS Techniques
				14.3.2.3 Use of GIS for Environmental Monitoring
		14.4 Integration of Geospatial Techniques of RS and GIS
		14.5 Application of Integration of Geospatial Techniques in Environment Monitoring System
			14.5.1 Application in Hydrological Modeling
		14.6 Case Study – Application of Integration of Geospatial Techniques in Hydrologic Modeling
			14.6.1 Description of Study Area
			14.6.2 Description of Hydrologic Model SHETRAN and SWAT
				14.6.2.1 SWAT Model
				14.6.2.2 SHETRAN Model
				14.6.2.3 Processing of Data in GIS for SWAT and SHETRAN Model Setup
				14.6.2.4 Post Processing of SWAT and SHETRAN Model Outputs in GIS
				14.6.2.5 Hydrologic Simulation
		14.7 Concluding Remarks
		References
	Chapter 15: Agent-Based Modeling for Integrated Urban Water Management
		15.1 Introduction
		15.2 Background
		15.3 Modeling and Simulation in Water Resource Management
		15.4 An Agent-Based Integrated Urban Water Management Framework
			15.4.1 Basic Framework and Issues Need to Be Addressed
			15.4.2 Agent Model-Based Framework
				15.4.2.1 ABM for Allocation of Total Water Availability (TAW)
				15.4.2.2 ABM for Water Resource Management
				15.4.2.3 Conceptual ABM Frame for Urban Water Development (UWD)
		15.5 Key Issues of Agent-Based Model for Implementation
			15.5.1 Mathematical Formulation
			15.5.2 Statistical Simulation
			15.5.3 Geospatial Simulation
		15.6 Concluding Remarks
		References
	Chapter 16: Data-Driven Modeling Approach in Model Rainfall-Runoff for a Mountainous Catchment
		16.1 Introduction
		16.2 Materials and Methods
			16.2.1 Study Area
			16.2.2 Physical Properties of the Soil
			16.2.3 Methods Used
				16.2.3.1 Polynomial Regression
				16.2.3.2 Linear Regression Model
				16.2.3.3 Quadratic Regression Model
				16.2.3.4 Non-Linear Regression
				16.2.3.5 Exponential Regression
				16.2.3.6 Logarithmic Regression
				16.2.3.7 Fuzzy Logic Approach
		16.3 Results and Discussion
			16.3.1 Polynomial Regression
			16.3.2 Linear Regression (LR) Method
			16.3.3 Quadratic Regression (QR) Method
			16.3.4 Exponential Regression (ER) Method
			16.3.5 Logarithmic Regression (LoR) Method
			16.3.6 Fuzzy Logic Method
		16.4 Comparison of Results
		16.5 Conclusions
		References
	Chapter 17: Geospatial Technology-Based Artificial Groundwater Recharge Site Selection for Sustainable Water Resource Management: A Case Study of Rajkot District, Gujarat
		17.1 Introduction
		17.2 Methodology
			17.2.1 Study Area
		17.3 Materials and Method
			17.3.1 Geology
			17.3.2 Rainfall Pattern
			17.3.3 Morphometric Analysis
			17.3.4 Land Use
			17.3.5 Soil Texture
			17.3.6 Drainage Frequency Density
			17.3.7 Slope Analysis
		17.4 Results and Discussions
			17.4.1 Lineament Analysis
			17.4.2 Normalized Weights
			17.4.3 Artificial Recharge Sites
			17.4.4 Status of Sample Recharge Sites
			17.4.5 Groundwater Potential Zoning
		17.5 Conclusions
		Websites
		References
	Chapter 18: Rainfall-Runoff Estimation for Rapti River Catchment Using Geospatial Technology
		18.1 Introduction
			18.1.1 Significance of the Research
			18.1.2 Objectives
		18.2 Data and Methods
			18.2.1 Study Area
			18.2.2 Data and Software Used
			18.2.3 Methodology
				18.2.3.1 Database
					18.2.3.1.1 Land Use / Land Cover
					18.2.3.1.2 Hydrological Soil Group
					18.2.3.1.3 Slope
					18.2.3.1.4 Rainfall
				18.2.3.2 Runoff Estimation from SCS-CN Method
					18.2.3.2.1 Determination of Weighted CN for Each Hydrological Response Unit
					18.2.3.2.2 Estimation of Antecedent Moisture Condition (AMC)
				18.2.3.3 Runoff Estimation for each LULC Category Using SCS-CN Method
					18.2.3.3.1 Determination of Weighted Curve Number for Each Land Use and Land Cover Category
					18.2.3.3.2 Computation of AMC-adjusted CN Values for Different Land Use and Land Cover Categories
					18.2.3.3.3 Computation of Runoff for Different Land Use and Land Cover Categories
					18.2.3.3.4 Types of Analyses Performed
		18.3 Results and Discussion
			18.3.1 Rainfall-Runoff Relationship for the Individual HRUs
			18.3.2 Rainfall-Runoff Relationship for the Individual Land Use and Land Cover Units
				18.3.2.1 AMC Wise
			18.3.3 Comparative Analysis between the Cumulative Runoff Estimated from HRUs and That from the Corresponding LULC Categories in the Individual Land Units Covered under the Influence of Each Rain Gauze Station
				18.3.3.1 AMC Wise
		18.4 Summary and Conclusion
			18.4.1 Summary of the Research Work
			18.4.2 Conclusions
		Acknowledgements
		References
	Chapter 19: Methodologies of Scenario Development for Water Resource Management: A Review
		19.1 Introduction
			19.1.1 Scenario Planning Perspective for Water Resources
			19.1.2 Basic Terminology
				19.1.2.1 Types of Scenarios
				19.1.2.2 Factors for Scenarios Planning
				19.1.2.3 Water Management
		19.2 Methodologies Applied
			19.2.1 Prediction/Derivation Methods
				19.2.1.1 System Dynamics (SD)
				19.2.1.2 Markov Model
				19.2.1.3 GAMLSS (Generalized Additive Model for Location, Scale, and Shape)
				19.2.1.4 ANN
				19.2.1.5 Water Balance Models
				19.2.1.6 PET (Potential Evapotranspiration) Models
				19.2.1.7 ISAT (Impervious Surface Analysis Tool)
				19.2.1.8 Rainfall-Runoff Models
				19.2.1.9 SWAT (Soil and Water Assessment Tool)
				19.2.1.10 Precipitation Runoff Modeling System (PRMS)
				19.2.1.11 Semi-distributed Land Use-based Runoff Processes (SLURP)
				19.2.1.12 Fuzzy Logic
				19.2.1.13 Water Indices
				19.2.1.14 Stochastic Programming
		19.3 Discussion
		19.4 Conclusions
		Acknowledgements
		References
Part IV: Future Algorithms in Environmental Systems
	Chapter 20: Process-Based Scenario Analyses of Future Socio-Environmental Systems: Recent Efforts and a Salient Research Agenda for Decision-Making
		20.1 Introduction
		20.2 Process-Based Modeling of Socio-Environmental Systems
			20.2.1 Defining a Socio-Environmental System
			20.2.2 Process-Based Modeling
		20.3 Scenario-Based Analysis
		20.4 Research Agenda for Decision-Making and the Way Forward
		Acknowledgements
		References
	Chapter 21: From Quantitative to Qualitative Environmental Analyses: Translating Mental Modeling into Physical Modeling
		21.1 Introduction
		21.2 Background
		21.3 Mental Model vs Physical Model
			21.3.1 Mental Model
			21.3.2 Physical Model
		21.4 Core Planning-Implementation Gaps of Modeling
			21.4.1 Dynamism of Environmental Model
			21.4.2 Ambiguity about What Constitutes Data
			21.4.3 Lack of Standard Collaboration Norms
			21.4.4 Modeling Uncertainty
			21.4.5 Integration of Quantitative and Qualitative Approach and Data Source
			21.4.6 Advancement in Scales and Scaling
			21.4.7 Human Dimensions
		21.5 The Translational Understanding: A Mental Model to a Physical Model
		21.6 Conclusion
		References
	Chapter 22: An Interdisciplinary Modeling Approach for Dynamic Adaptive Policy Pathways
		22.1 Introduction
		22.2 Background
		22.3 Interdisciplinary Modeling Approach for Policy Pathways
			22.3.1 Perspectives and Opportunities for Air Pollution
			22.3.2 Developing an Interdisciplinary Approach
			22.3.3 Field Missions and Long-Term Monitoring
		22.4 Computational Interventions in Policy Pathways
		22.5 Moving Forward and Conclusions
		References
Index