Modeling and Simulation of Environmental Systems: A Computation Approach

✍ Scribed by Satya Prakash Maurya (editor), Akhilesh Kumar Yadav (editor), Ramesh Singh (editor)

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 377
• Edition: 1
• Category: Library

✦ Synopsis

This book presents an overview of modeling and simulation of environmental systems via diverse research problems and pertinent case studies. It is divided into four parts covering sustainable water resources modeling, air pollution modeling, Internet of Things (IoT) based applications in environmental systems, and future algorithms and conceptual frameworks in environmental systems. Each of the chapters demonstrate how the models, indicators, and ecological processes could be applied directly in the environmental sub-disciplines. It includes range of concepts and case studies focusing on a holistic management approach at the global level for environmental practitioners.

Features:

• Covers computational approaches as applied to problems of air and water pollution domain.
• Delivers generic methods of modeling with spatio-temporal analyses using soft computation and programming paradigms.
• Includes theoretical aspects of environmental processes with their complexity and programmable mathematical approaches.
• Adopts a realistic approach involving formulas, algorithms, and techniques to establish mathematical models/computations.
• Provides a pathway for real-time implementation of complex modeling problem formulations including case studies.

This book is aimed at researchers, professionals and graduate students in Environmental Engineering, Computational Engineering/Computer Science, Modeling/Simulation, Environmental Management, Environmental Modeling and Operations Research.

✦ Table of Contents

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