Water and Energy Management in India : Artificial Neural Networks and Multi-Criteria Decision Making Approaches

This book provides an innovative, realistic and reliable solution to the common problem of Indian water and energy sector due to the onset of the Impact of Climate Change and Large-Scale Urbanization. Twelve Case Studies and One Review Paper that were included in this book depict the way soft computation techniques, simulation and decision-making framework can optimize the best solution from multiple solutions to the problems of water and energy management which corresponds to a novel symbiotic and synchronous nexus between water and the energy sector. All the studies included in this book are collected from all parts of India. The selected studies utilized the latest technologies like Multi-Criteria Decision Frame Work, Neural Networks and Nature-Based Optimization techniques to achieve diverse objectives from the prediction of climatic parameters to yield from ungauged watershed to performance optimization of Water Treatment Plant, Hydropower as well as futuristic alternative energy systems like Wave to Power Plants. Preface Management of Water and Energy Interdependencies in World Management of Water and Energy Interdependencies in India References Acknowledgements Introduction Introduction 1.1 Present Scenario of Water Resources 1.2 Present Scenario of Energy Resources 1.3 Climate Change 1.3.1 Climate Change Effect on Water Resources 1.3.2 Impact of Climate Change on Energy Resources 1.4 Soft Computation Techniques 1.4.1 Artificial Neural Networks 1.4.2 Fuzzy Logic 1.4.3 Genetic Algorithm 1.4.4 Particle Swarm Optimization 1.5 Multi-criteria Decision-Making Techniques 1.5.1 Analytical Hierarchy Process 1.5.2 Analytic Network Process References Contents Water Management A Review of Multiple Criteria Decision-Making Methods in Reference to Water Resources and Climate Science Applications 1 Introduction 2 Multiple Criteria Decision Making 3 MCDM Framework 4 MCDM Methods 4.1 Multi-attribute Utility Theory (MAUT) 4.2 Analytic Hierarchy Process (AHP) 4.3 Fuzzy Theory 4.4 Case-Based Reasoning (CBR) 4.5 Data Envelopment Analysis (DEA) 4.6 Simple Multi-attribute Rating Technique (SMART) 4.7 Goal Programming 4.8 Elimination and Choice Translating Reality (ELECTRE) 4.9 Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) 4.10 Simple Additive Weighting (SAW) 4.11 Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS) 5 Summary and Conclusion References Development of Spatial Cognitive Model for Estimation of Ungauged Runoff for Mesoscale Rivers 1 Introduction 1.1 Global Scenario of Urbanization 1.2 Landuse Status of India 2 Literature Review 2.1 Objective of the Study 3 Study Location 3.1 Geology 3.2 Topography 3.3 Drainage 3.4 Climate 3.5 Soil 3.6 Landuse and Land Cover 3.7 Water Quality 4 Methods Applied 4.1 Analytic Hierarchy Process (AHP) 4.2 Fuzzy MCDM Method 4.3 Weighted Sum Model (WSM) 4.4 Weighted Product Model (WPM) 4.5 Artificial Neural Network Model (ANN) 5 Methodology 5.1 Data Compilation 5.2 Image Processing 5.3 Hydrologic Engineering Center—Hydrologic Modelling System (HEC-HMS) for Runoff 5.4 Determination of Priority Value Using Multi Criteria Decision Making (MCDM) 5.5 Development of the Cognitive Index for Representing Runoff Probability 5.6 Validation of the Model 6 Results and Discussion 6.1 Results from Image Processing 6.2 Results from MCDM 6.3 Validatio of the Model Results 6.4 SWOT Analysis of the Index 7 Conclusion References Indicator Based Impact Analysis of Urbanization with Respect to Evapo-Transpiration 1 Introduction 2 Methods Used 2.1 The MCDM Approach 2.2 Analytical Hierarchy Process 2.3 Weighted Sum Method 2.4 Weighted Product Method 2.5 Artificial Neural Networks 3 Case Studies 3.1 Agartala (23.8333° N, 91.2667° E) 3.2 Kolkata (22.5667° N, 88.3667° E) 3.3 Brisbane (27.4679° S, 153.0278° E) 3.4 New York City (40.6700° N, 73.9400° W) 4 Methodology in Detail 4.1 Study Methodology 4.2 Application of the MCDM 4.3 Development of Vulnerability Index 4.4 Application of Neural Network and Genetic Algorithm 4.5 Preparation of the Training Dataset 4.6 Topology Identification 4.7 Training the Network 5 Validation of the Framework 5.1 Sensitivity Analysis 5.2 Compare with Other Similar Methods 5.3 Case Study Analysis 6 Results and Discussions 6.1 MCDM Results 6.2 Vulnerability Index 6.3 Results from the Neuro Genetic Model 7 Validation of the Model 7.1 Sensitivity Analysis 8 Compare with Other Similar Methods Show the Results from AHP, WSM and WPM 8.1 Weighted Sum Method (WSM) 8.2 Weighted Product Method (WPM) 9 Case Study Analysis 10 Conclusion References Trend Analyses in Groundwater Levels of the Bikaner District, Rajasthan 1 Introduction 2 Study Area 3 Data Collection 4 Methodology 4.1 Mann Kendall Test 4.2 Sen’s Slope Test 4.3 Mann Kendall Test with Correction Factor-2 4.4 Innovative Trend Analysis Plot 5 Results and Discussions 5.1 Trend Analyses in Monsoon Groundwater Level Time Series (1994–2018) of Six Blocks in the Bikaner District of the Rajasthan 5.2 Trend Analyses in Post-monsoon Kharif Groundwater Level Time Series (1994–2018) of Six Blocks in the Bikaner District of the Rajasthan 5.3 Trend Analyses in Post-monsoon Rabi Groundwater Level Time Series (1994–2018) of Six Blocks in the Bikaner District of the Rajasthan 5.4 Trend Analyses in Pre-monsoon Groundwater Level Time Series (1994–2018) of Six Blocks in the Bikaner District of the Rajasthan 6 Conclusions References Climate Change Impact on Virtual Water Availability: A Categorized Polynomial Neural Network Approach 1 Introduction 1.1 Global Scenario of Virtual Water Availability 1.2 The Criticality of Virtual Water Concept 1.3 Motivation 1.4 Objective and Novelty 2 Methodology 2.1 Development of the Virtual Water Availability Prediction Model 2.2 Impact of Climate Change on Virtual Water Availability 3 Results and Discussions 4 Conclusion References Development of ANN Model for Simulation of the Runoff as Affected by Climatic Factors on the Jamuna River, Assam, India 1 Introduction 2 Materials and Methods 2.1 Study Area 2.2 Data Collection 2.3 Data Analysis 2.4 Development of an ANN-Based Runoff Forecasting Model 3 Results 4 Discussion and Conclusions References Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network 1 Introduction 2 Data and Case Study 3 Materials and Methods 3.1 FAO-56 Penman-Monteith Method 3.2 Turc Method 3.3 Priestly and Taylor Method 3.4 Hargreaves Method 3.5 Artificial Neural Networks (ANN) 3.6 Performance Metrics 4 Results and Discussions 5 Conclusions References Verifying Storm Water Drainage System Capacity for Vadodara Airport 1 Introduction 1.1 Flooding of Airports 1.2 Significance of the Study 2 Study Area and Data Collection 3 Methodology 3.1 Storm Water Management Model (SWMM) 3.2 Methodology of SWMM Model Development 4 Result & Analysis 4.1 Results for Sub-Catchments 4.2 Results of Nodes 4.3 Floodplain Mapping Using INP-PINS 5 Conclusion References Energy Management Optimal Trade-Off Between the Energy—Economy of a Hydropower Plant for Better Management of the Renewable Energy Resources 1 Introduction 2 Detail Methodology 2.1 Determination of Priority Value 2.2 Application of Optimization Techniques (OT) for Identification of Optimal Trade-Off 3 Results and Discussions 3.1 Results from MCDM 3.2 Identification of Trade-Off Between the UEI and FLI 4 Conclusion References Impact Analysis of Water, Energy, and Climatic Variables on Performance of Surface Water Treatment Plants 1 Introduction 1.1 Study Objective 1.2 Literature Reviews 2 Methods Used 2.1 Multiple Criteria Decision-Making Methods (MCDM) 3 Methodology 3.1 Application of MCDM 3.2 Development of Index 3.3 Application of Polynomial Neural Network 4 Result and Discussion 4.1 Results from the ANP Method 4.2 Results from the PNN Method 4.3 Results from the Case Study 5 Conclusion References Power Allocation in an Educational Institute in India: A Fuzzy-GMDH Approach 1 Introduction 1.1 Global Energy Scenario 1.2 Present Consumption Pattern 1.3 Problem Areas 1.4 Indian Energy Scenario 1.5 Proposed Solution 1.6 Prioritization of Energy Consumers 1.7 Optimization of Energy Allocation 1.8 Global Water Scenario 1.9 Indian Water Scenario 1.10 The Objective of the Present Investigation 2 Methods Used 2.1 Particle Swarm Optimization 2.2 Differential Evolution 2.3 Neuro-genetic Optimization 2.4 Fuzzy Logic 2.5 Group Method of Data Handling 3 Case Studies 3.1 About the Study Area 3.2 Justification 4 Methodology in Detail 5 Results and Discussions 5.1 Prioritizations of Consumers by Fuzzy Logic 6 Conclusion References Application of New Convergent Point Decision Making Method in Estimation of Vulnerability Index for Hydro Power Reservoirs 1 Introduction 1.1 Objective 2 Methods Adopted 2.1 Analytical Network Process 2.2 Measuring Attractiveness by a Categorical Based Evaluation Technique 2.3 Group Method of Data Handling (GMDH) 3 Detailed Methodology 3.1 Application of the New Convergent Point MCDM Methods and GMDH to Determine the P.V. of the Selected Parameters 3.2 Determination of Vulnerability Index 3.3 Validation of the Model 4 Results and Discussion 5 Scenario Analysis 6 Conclusion References Recognition of Fatigue Failure in Wave Energy Converter Using Statistical Control Chart, Multi-criteria Decision Making Tools and Polynomial Neural Network Model 1 Introduction 2 Objective of the Study 3 Fatigue Influenced Factors 3.1 Internal Structure 3.2 External Structures 3.3 Non-accessible Areas 3.4 Wave Impact, Wave Climate and Weather Condition 3.5 Replacement Schedule for Bearings System 3.6 The Manufacturer Defined Range Rating 3.7 Foundation Design for Wave Energy Converters 4 Methodology 5 Results and Discussion 5.1 Application of Polynomial Neural Network 5.2 Sample Model Data Generated from GMDH Software 6 Conclusion References Conclusion The Water and Energy Management in India provides an innovative, realistic and reliable solution to the common problem of Indian Water and Energy Sector due to the onset of the Impact of Climate Change and Large Scale Urbanization. Twelve Case Studies and One Review Paper that was included in this monograph depicts the way soft computation techniques, simulation and decision making framework can optimize the best solution from multiple solutions to the problems of water and energy management which corresponds to a novel symbiotic and synchronous nexus between Water and the Energy Sector. All the studies included in this monograph is collected from all parts of India. The selected studies utilized the latest technologies like Multi-Criteria Decision Frame Work, Neural Networks, Nature-Based Optimization techniques to achieve diverse objectives from the prediction of climatic parameters to yield from ungauged watershed to performance optimization of Water Treatment Plant, Hydropower as well as futuristic alternative energy systems like Wave to Power Plants.