ICT for Smart Water Systems: Measurements and Data Science 3030619729, 9783030619725

Table of contents :
Series Preface
Preface
References
Contents
Data Science Trends and Opportunities for Smart Water Utilities
1 Introduction
1.1 Data Rich Information Poor: Fixing the DRIP
1.2 Big Data and Analytics Opportunities
1.3 Hydroinformatics
2 Data Analytics: Prediction
3 Data Analytics: Classification
3.1 Internet of Things and Edge Computing
4 Cloud Computing and Condition Monitoring
5 Digitalisation
6 Blockchain, Data Sharing and Web 3.0
7 Smart Networks
8 Use of Smart Meter Data
9 Discussion of Technology Adoption and Recommendations
10 Summary
References
Review of Techniques for Optimal Placement of Pressure and Flow Sensors for Leak/Burst Detection and Localisation in Water Dis...
1 Introduction
2 Synthesis and Analysis of Optimal Sensor Placement Techniques for Leak/Burst Detection and Localisation
3 Considerations on Specific Issues Encountered When Developing Optimal Sensor Placement Techniques for Leak/Burst Detection a...
3.1 Model Uncertainties and Sensitivity to the Leak/Burst Size Assumed for Hydraulic Simulations
3.2 Measurement Uncertainties
3.3 Sensor and Communication Failures
3.4 Use of Flow Sensors
3.5 Accounting for Risk
4 Discussion
5 Conclusions
References
A Bird´s-Eye View of Data Validation in the Drinking Water Industry of the Netherlands
1 Introduction
1.1 Background
1.2 Scope and Approach
1.3 Outline
2 Data Quality Control: Theory and Current Practice
2.1 Principles of Data Quality Control
2.2 The Role of Validation in Data Quality Control
2.3 Data Quality Control in the Context of Drinking Water
2.4 Current Implementation of Data Validation Techniques by the Drinking Water Companies
2.5 Data Validation: Experiences of a Front Runner - Company D
3 Literature Review on Faulty Data Detection Techniques for Water Utilities
3.1 Background
3.2 Faulty Data Detection Techniques
3.2.1 Simple Tests
3.2.2 Statistical Tests
Comparison of Flow Pattern Distributions (CFPD)
Spatial Deviation
Extreme Value Checks
Regression Analysis, Correlation Checks and Principal Component Analysis (PCA)
3.2.3 Data-Driven Models
3.2.4 Physical Models
3.2.5 Knowledge-Based Techniques
4 Application of Data Validation
4.1 Overview of the Data and Selection of the Techniques
4.2 Data and Proposed Data Validation for Company A
4.3 Results Obtained
4.3.1 Water Quality Data at WWTP I and II
4.3.2 Pumping Stations Data
4.4 Anomalies Within a Day
4.4.1 Finding Faulty Data by Comparison. Water vs Energy
4.5 Best Practices and Issues in Data Validation Identified in the Case Studies
5 Discussion and Recommendations for Future Work
5.1 Introduction
5.2 Recommendation Regarding Future Work
5.2.1 Standardization
Data Model
Data Collection Redundancy
Aggregation of Data
Data Correction Techniques
Data Reconciliation
5.2.2 Selection of Faulty Detection Techniques
5.2.3 Modelling for Anomaly Identification
6 Conclusions
6.1 Specific Conclusions Based on the Cases Regarding Faulty Data Detection
6.2 General Conclusions Regarding Data Quality Control
References
Monitoring and Controlling a Smarter Wastewater Treatment System: A UK Perspective
1 A Systematic Approach
2 Smart Wastewater Network
2.1 Philosophy of Operation
2.2 Monitoring the Inputs into the System
2.3 Monitoring the Network and Outputs from the System
2.4 The Opportunities and Barriers for the Smart Wastewater Network
3 Smart Wastewater Treatment
3.1 Philosophy of Operation
3.2 Measuring and Controlling the Wastewater Treatment Process
3.2.1 Measuring and Controlling Preliminary and Primary Treatment Processes
3.2.2 Measuring and Controlling Secondary Treatment
3.2.3 Measuring and Controlling Sludge and Resource Recovery Processes
3.3 Holistic Control: Model-Based and Multivariate Process Control
4 A Smarter Wastewater Industry
References
Using Radial Basis Function for Water Quality Events Detection
1 Introduction: The Problem of Water Quality Events Classification
2 RBF: Structure and Basic Description
3 RBF Parameters Selection
4 An Illustrative Case Study
5 Real-World Data Analysis
6 Using Other Kernel Functions for RBF
7 Concluding Remarks
Appendix: Result of Run 1
References
Promoting Smart Water Systems in Developing Countries Through Innovation Partnerships: Evidence from VIA Water-Supported Proje...
1 Introduction
2 ICTs and Water Management in Developing Countries
3 Theoretical Context and Focus of the Study
3.1 Innovation Partnerships
3.2 Open Innovation and Innovation System Theories
3.3 Structural and Relational Perspectives on Partnerships
3.4 Focus of the Study
4 Methodology
4.1 Selection of Cases
4.2 Data Collection and Analysis
5 Results and Analysis
5.1 Characteristics of VIA Water ICT-WIPs
5.1.1 Nature of Innovations
5.1.2 Formation of ICT-WIPs
5.2 Resources Exchange in VIA Water ICT-WIPs
5.2.1 Exchange of Immaterial Resources
5.2.2 Exchange of Material Resources
5.3 Governance Mechanisms of VIA Water WIPs
5.3.1 Structural Governance Mechanisms
5.3.2 Relational Governance Mechanisms
5.4 Some Challenges for the VIA Water ICT-WIPs
6 Discussion
7 Conclusions
7.1 Innovation Partnerships and Smart Water Systems in Developing Countries
7.2 Practical Implications
7.3 Recommendations
7.3.1 Recommendations for Future Research
7.3.2 Recommendations for VIA Water
Annex: Overview of Lead Innovators, Type of Organisations They Are and Their Country of Origin
References
Website
Exploring Assimilation of Crowdsourcing Observations into Flood Models
1 Introduction
2 Crowdsourced Observations
3 Case Studies and Water-Related Models
3.1 Brue Catchment (UK)
3.2 Bacchiglione Catchment (Italy)
4 Model Updating Techniques
4.1 Kalman Filter
4.2 Ensemble Kalman Filter
4.3 Synthetic Flow Observations
4.4 Estimation of the Observational Error
5 Assimilation of Flow Observations from Static Heterogeneous Sensors
5.1 Assimilation of Synchronous Observations
5.1.1 Assimilation of Flow Observations Only from Social Sensors
5.1.2 Assimilation of Flow Observations from Both Physical and Social Sensors
5.2 Assimilation of Asynchronous Observations
5.2.1 Assimilation of Flow Observations Only from Social Sensors
5.2.2 Assimilation of Flow Observations from Both Physical and Social Sensors
6 Conclusions
References
Precipitation Measurement with Weather Radars
1 Introduction
2 Sources of Uncertainty in the Estimation of Precipitation with Radar
2.1 Radar Calibration
2.2 Echoes Due to Non-meteorological Origin
2.3 Attenuation
2.4 Variations in the Vertical Profile of Reflectivity
2.5 Variations of the DSD and Radar Rainfall Estimation
3 Adjusting Radar Rainfall with Raingauge Measurements
4 Applications of Weather Radar
4.1 Radar-Based Precipitation Forecasting
4.2 Hydrological Applications
5 Concluding Comments
References
Satellite Remote Sensing of Soil Moisture for Hydrological Applications: A Review of Issues to Be Solved
1 Introduction
2 Soil Moisture Measuring Methods
2.1 In Situ Instruments
2.2 Satellite Remote Sensing
2.2.1 Optical
2.2.2 Thermal Infrared
2.2.3 Passive and Active Microwaves
2.2.4 Satellite Missions
3 Hydrological Evaluation of Satellite Soil Moisture
4 SMOS Descending and Ascending Overpasses
5 Error Distribution Modelling of SMOS Soil Moisture Measurements
6 The Need for New Hydrological Soil Moisture Product Development
7 Discussion and Conclusions
References
Spectroscopic Methods for Online Water Quality Monitoring
1 Introduction
2 Spectroscopy
3 Interaction of Light and Matter
4 Signal Treatment
4.1 Data Validation
4.2 Transformations
4.3 Chemometrics
5 In Situ Spectroscopy for Water and Wastewater Analysis
5.1 UV/Vis Absorption Spectroscopy
5.1.1 Sum Organic Parameters
5.1.2 Nitrate and Nitrite
5.1.3 Colour
5.1.4 Turbidity and Suspended Solids
5.1.5 Other Direct Parameters
5.1.6 Indirect Parameters
5.1.7 Spectral Fingerprint and Contamination Alarm
5.1.8 Remote Sensing
5.2 Fluorescence Spectroscopy
5.2.1 Algal Pigments
5.2.2 Dissolved Organic Matter
5.2.3 Wastewater
5.2.4 Oil in Water
5.3 NIR
5.4 Further Optical Technologies with Potential for Online Use in Smart Water Systems
5.4.1 Raman Spectroscopy
5.4.2 Laser-Induced Breakdown Spectroscopy
5.4.3 Refractive Index
5.4.4 Image Analysis
6 Discussion
7 Outlook
References
Quartz Crystal Microbalance Sensors: New Tools for the Assessment of Organic Threats to the Quality of Water
1 Introduction
2 Theory and Modeling of QCM Data
2.1 Sauerbrey´s Equation: Rigid Mass
2.2 Sauerbrey´s Mass Sensitivity
2.3 Kanazawa: Gordon Equation: Quartz Crystal in Contact with a Liquid
2.4 Small Load Approximation: The Electromechanical Model
2.5 Semi-Infinite Viscoelastic Layer Newtonian Liquid
2.6 Purely Inertial Layer: Sauerbrey´s Equation
2.7 Viscoelastic Layer of Arbitrary Thickness
2.8 Viscoelastic Layer in Liquid
3 QCM Detection Scheme and Electronic Interfaces
3.1 Quartz Oscillators
3.2 Network or Impedance Analysis
3.3 Functionalization Methods of the QCM Gold Surface
3.4 Detection Step
4 Conclusions
References