📈 Basic Information about Digital Enablers
| Digital Enablers | Short Description | Applications | Example of Authors (Year) |
|---|---|---|---|
| Big Data | Scalable storage and processing (cloud, Hadoop, Spark, streaming). | Collect and process exposure and hazard data required for assessing aquatic risk; Could improve the performance of learning models in the prediction of water quality; Drought monitoring; Predict Hazard Wastewater generation; Management of water resources; Protect from cyberattacks, and also improves energy management of in smart grid; Rapid flood mapping; Rapid disaster response (flooding); Remote Sensing for Water Environment Monitoring; Water environment monitoring (water mapping, quantitative estimation of key water quality parameters, flood and drought monitoring, marine surveying and mapping) | Cian et al. (2018), Sun and Scanlon (2019), Chen et al. (2020), Chen et al. (2022), Cheng et al. (2023), Kamyab et al. (2023), Xie et al. (2024), Balamurugan et al. (2025) |
| Blockchain / DLT | Distributed ledger ensuring data and transaction integrity. | Create an efficient trust mechanism among the links in the process of water resource utilization. Development and management of smart contracts, incidents, and supplies of urban water supply and sanitation systems; Digitized quality certificates and consumption information; Industrial Wastewater Management; Intelligent and secure smart watering system (Blockchain technique is used to provide secure access in the Internet of Things (IoT) enabled system by allowing only the trusted devices to access and manage the proposed SWS); Reliable and secure storage of water resource data, high efficiency of information transmission, and high traceability of water quality problems; Smart Flood Detection; Smart water conservation; Solid waste classification, Third-party validation and oversight in water systems | Munir et al. (2019), Hakak et al. (2020), Thakur et al. (2021), Asgari and Nemati (2022), Xia et al. (2022), Alsumayt et al. (2023), Furones and Monzón (2023), Alabdali (2025) |
| Cloud Computing | Provide scalable storage and computing so AI can continuously process large environmental datasets from sensors, satellites and infrastructure. | Automatic Mapping and Monitoring of Marine Water Quality Parameters; Disaster analysis system of multi-sourced data streams; Cloud GIS is used for disaster risk assessment, leveraging real-time data feeds to predict flood zones and monitor storm impacts; Predicting impending water pollution outbreaks such as algal blooms; Real-time management of water resources; Water environment monitoring (water mapping, quantitative estimation of key water quality parameters, flood and drought monitoring, marine surveying and mapping) | Kurtz et al. (2017), Huang et al. (2017), Sagan et al. (2020), Chen et al. (2022), Kwong et al. (2022), Li et al. (2025) |
| Digital Twin | Live, data‑connected virtual replica of a physical system. | Dam and Watershed Management Platform; Management of drinking water distribution networks; Forecasting floods; Management of System Efficiency in Water Distribution Networks; Monitoring water distribution system; Simulate the performance of green roofs under various weather conditions; Wastewater Treatment. | Fuertes et al. (2020), Bonilla et al. (2022), Ramos et al. (2023), Park and You (2023), Roudbari et al. (2024), Wang et al. (2024), Leung and Suzuki (2025) |
| IoT / Sensing Infrastructure | IoT = a full connected system that includes sensors plus networking, data transmission, cloud/edge processing, and sometimes automated actions; SI = the sensors/actuators themselves. | Detecting hazardous substances in the environment; Environmental pollution monitoring and management; Facilitate real-time monitoring of pipeline conditions, early issue detection, and efficient maintenance in Sewer Management. It also enables real-time detection of blockages, leaks, and flooding risks; Monitor water quality and provide early warnings about contamination, enabling proactive interventions to improve usability and safety; Reduce stormwater runoff flow; River Water Quality Monitoring System; Smart water quality monitoring system; Water quality monitoring. | Chowdury et al. (2019), Pasika and Gandla (2020), Lakshmikantha et al. (2021), Essamlali et al. (2024), Popescu et al. (2024), Leung and Suzuki (2025), Kang et al. (2025) |
| Open Data | Publicly accessible datasets and APIs. | Ensuring data is accurate, timely, and aligned with the FAIR principles (Findable, Accessible, Interoperable, and Reusable); Evaluate and map the potential of waste heat from sewage water; Hydrological models' integration, evaluation and application; Predicting Urban Waterlogging Risks | Pelda and Holler (2018), Salas et al. (2020), Tran et al. (2020), Open Data Charter (2025), UNESCO and Ziesche (2023) |
📄 Access Authors Document
| Main Authors (Year) | Title | Resume | Link |
|---|---|---|---|
| Alabdali (2025) | Blockchain based solid waste classification with AI powered tracking and IoT integration | Presents an AI-driven waste classification system integrating IoT and Blockchain for smart cities. While primarily focused on solid waste management, it has indirect water relevance through preventing pollution from improper waste disposal that could contaminate water sources and waterways. IoT-connected bins use blockchain for secure, transparent data storage, with ML/DL algorithms for real-time waste classification to optimize collection and recycling efficiency. | 🔗 |
| Alsumayt et al. (2023) | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones | Proposes a secure Flood Detection Secure System (FDSS) for Saudi Arabia using UAV drones with deep active learning and blockchain integration. Addresses flooding risks from climate change and rapid water level changes in dams. Uses blockchain-based federated learning with homomorphic encryption for privacy-protected flood monitoring, enabling real-time estimation of flooded areas, tracking dam water levels, and rapid disaster response to flood threats. | 🔗 |
| Asgari and Nemati (2022) | Application of Distributed Ledger Platforms in Smart Water Systems — A Literature Review | Comprehensive literature review examining distributed ledger technology (DLT) and blockchain applications in water management. Organizes studies into three main application areas: Smart Water Systems, Water Quality Monitoring, and Storm Water Management. Addresses technical, organizational, social, and institutional challenges hindering blockchain adoption in water sector. Emphasizes need for long-term commitment, updated policies, and expertise development for successful implementation of blockchain in water resource management. | 🔗 |
| Balamurugan et al. (2025) | Role of artificial intelligence in smart grid – a mini review | Mini review on AI's role in smart grid systems. While focused on energy management, has water implications through energy-water nexus, particularly for water treatment plants and pumping systems. Discusses how AI improves energy efficiency in smart grids, which includes water infrastructure that requires significant energy for treatment, distribution, and wastewater processing. Also addresses cybersecurity protection for critical infrastructure including water systems. | 🔗 |
| Bonilla et al. (2022) | A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation | Creates live, data-connected virtual replica of physical water distribution system enabling real-time monitoring, performance optimization, and predictive maintenance. The digital twin approach allows simulation of different operational scenarios to improve system efficiency and reliability in drinking water distribution. | 🔗 |
| Chen et al. (2020) | Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data | Evaluates performance of different ML algorithms in predicting water quality parameters and identifies key water quality indicators. Demonstrates how big data analytics can enhance water quality assessment, enable early detection of pollution events, and support proactive water management decisions for protecting aquatic ecosystems and public health. | 🔗 |
| Chen et al. (2022) | Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects | Covers water mapping, quantitative estimation of key water quality parameters, flood and drought monitoring, and marine surveying. Discusses current status, challenges, and opportunities in using satellite imagery and remote sensing technologies for large-scale, continuous water resource monitoring and environmental assessment. | 🔗 |
| Cheng et al. (2023) | Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era | Uses advanced data analytics to identify and assess hazardous substances in aquatic environments from large datasets. Demonstrates how big data and text mining can improve detection of emerging contaminants, predict aquatic risks, and support evidence-based water quality management and pollution control strategies. | 🔗 |
| Chowdury et al. (2019) | IoT Based Real-time River Water Quality Monitoring System | Develops IoT-based real-time river water quality monitoring system using networked sensors. Enables continuous monitoring of water quality parameters, early warning of contamination events, and data-driven decision making for river management. System provides stakeholders with accessible real-time information for protecting water resources and responding quickly to pollution incidents. | 🔗 |
| Cian et al. (2018) | Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data | Introduces Normalized Difference Flood Index (NDFI) for rapid flood mapping using Earth observation satellites and big data. Leverages satellite imagery processing for quick identification and extent mapping of flood events. Supports rapid disaster response, damage assessment, and emergency management during flooding events through timely geospatial information. | 🔗 |
| Essamlali et al. (2024) | Advances in machine learning and IoT for water quality monitoring: A comprehensive review | Comprehensive review of machine learning and IoT advances for water quality monitoring. Examines integration of sensor networks, ML algorithms, and cloud computing for real-time water quality assessment. Highlights how combined IoT-ML approaches enable continuous monitoring, automated anomaly detection, and predictive analytics for maintaining safe water quality in distribution systems. | 🔗 |
| Fuertes et al. (2020) | Building and exploiting a Digital Twin for the management of drinking water distribution networks | Presents methodology for building and exploiting digital twins for drinking water distribution network management. Digital twin provides virtual representation synchronized with real network, enabling performance optimization, leak detection, and operational planning. Supports data-driven decision making for maintaining water quality, reducing losses, and improving service reliability in urban water supply. | 🔗 |
| Furones and Monzón (2023) | Blockchain applicability in the management of urban water supply and sanitation systems in Spain | Analyzes how blockchain can improve transparency, traceability, and third-party validation in water systems. Discusses potential for digitized quality certificates, consumption information management, secure incident reporting, and enhanced accountability in municipal water services. | 🔗 |
| Hakak et al. (2020) | Industrial Wastewater Management using Blockchain Technology: Architecture, Requirements, and Future Directions | Addresses requirements for secure, transparent tracking of wastewater generation, treatment, and discharge in industrial settings. Blockchain ensures immutable record-keeping, regulatory compliance verification, and stakeholder accountability in industrial water pollution control. | 🔗 |
| Huang et al. (2017) | A cloud-enabled automatic disaster analysis system of multi-sourced data streams | Integrates cloud computing with real-time data from various sources for disaster monitoring and response. Applicable to water-related disasters including floods, enabling rapid analysis of disaster extent, impact assessment, and coordination of emergency response efforts. | 🔗 |
| Kamyab et al. (2023) | The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management | Discusses how AI/big data technologies can address complex environmental challenges including water scarcity, quality degradation, and climate change impacts on water resources. Explores applications in pollution monitoring, resource optimization, and predictive analytics. | 🔗 |
| Kang et al. (2025) | Trends in intelligent sensor-based customized management technologies for sewer infrastructures | Examines trends in intelligent sensor-based customized management technologies for sewer systems. Discusses IoT sensors, AI analytics, and smart technologies for real-time sewer monitoring, blockage detection, overflow prevention, and maintenance optimization. Enables proactive sewer management, reducing flooding risks and environmental contamination from sewage spills. | 🔗 |
| Kurtz et al. (2017) | Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources | Combines computational models with cloud infrastructure for processing large environmental datasets, enabling dynamic water management, flood forecasting, and drought monitoring. Cloud-based approach provides scalable computing power for complex hydrological simulations. | 🔗 |
| Kwong et al. (2022) | Automatic Mapping and Monitoring of Marine Water Quality Parameters in Hong Kong Using Sentinel-2 Image Time-Series and Google Earth Engine Cloud Computing | Leverages satellite imagery, automated processing, and cloud infrastructure for continuous assessment of coastal water quality. Enables early detection of harmful algal blooms, pollution events, and environmental changes affecting marine ecosystems. | 🔗 |
| Lakshmikantha et al. (2021) | IoT based smart water quality monitoring system | Uses affordable sensors and wireless communication for continuous water quality measurement. Makes water quality monitoring accessible for resource-limited settings, enabling communities to track drinking water safety and environmental water quality at lower cost. | 🔗 |
| Leung and Suzuki (2025) | The Potential of Green Infrastructure and Artificial Intelligence in Urban Stormwater Management | Discusses how AI can optimize green roof performance, predict system behavior under various weather conditions, and enhance hybrid systems combining green infrastructure with traditional drainage. Digital twin simulations enable testing resilience of stormwater solutions before implementation. | 🔗 |
| Li et al. (2025) | GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS | Examines GIScience in AI era, focusing on autonomous and intelligent geographic systems for environmental monitoring. Discusses Cloud GIS for disaster risk assessment, real-time flood zone prediction, and storm impact monitoring. Integration of AI with geospatial analysis enhances water-related hazard assessment and emergency response planning. | 🔗 |
| Munir et al. (2019) | An intelligent and secure smart watering system using fuzzy logic and blockchain | Combines AI-based irrigation control with blockchain security for IoT-enabled agricultural water management. Ensures only trusted devices access the smart watering system while optimizing water use efficiency through intelligent decision-making algorithms. | 🔗 |
| Open Data Charter (2025) | An anthology of AI and Open Data resources | Anthology of AI and open data resources emphasizing FAIR principles (Findable, Accessible, Interoperable, Reusable) for data management. Discusses importance of open data for AI applications in various domains including water resources. Advocates for transparent, accessible data sharing to enable innovation, accountability, and evidence-based water management decisions. | 🔗 |
| Park and You (2023) | A Digital Twin Dam and Watershed Management Platform | Creates virtual representation of dam and watershed systems for real-time monitoring, flood forecasting, and operational optimization. Enables simulation of various scenarios, predictive maintenance, and improved decision-making for dam safety and water resource management in river basins. | 🔗 |
| Pasika and Gandla (2020) | Smart water quality monitoring system with cost-effective using IoT | Employs affordable sensors, microcontrollers, and wireless communication for continuous water quality measurement and remote monitoring. Democratizes water quality monitoring by making technology accessible to communities, schools, and organizations with limited budgets. | 🔗 |
| Pelda and Holler (2018) | Methodology to evaluate and map the potential of waste heat from sewage water by using internationally available open data | Identifies opportunities for energy recovery from wastewater treatment, supporting water-energy nexus optimization. Uses publicly available data to assess feasibility of heat extraction from sewage for district heating and other applications. | 🔗 |
| Popescu et al. (2024) | Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management | Reviews AI and IoT-driven technologies for environmental pollution monitoring and management. Covers air, water, and soil pollution detection using sensor networks and ML algorithms. Discusses how integrated IoT-AI systems enable real-time environmental surveillance, early warning systems, and automated responses to pollution events. | 🔗 |
| Ramos et al. (2023) | Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks | Examines smart water grids and digital twin for system efficiency management in water distribution networks. Integrates IoT sensors, data analytics, and digital twin technology for optimizing water distribution, reducing leakage, and improving operational efficiency. Enables predictive maintenance and real-time performance monitoring of water infrastructure. | 🔗 |
| Roudbari et al. (2024) | From data to action in flood forecasting leveraging graph neural networks and digital twin visualization | Applies graph neural networks and digital twin from data to action in flood forecasting. Uses advanced AI techniques to predict flood events, assess risks, and support emergency response planning. Digital twin approach enables testing intervention strategies and optimizing flood management actions before implementation. | 🔗 |
| Sagan et al. (2020) | Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing | Reviews remote sensing for monitoring inland water quality, examining potential and limitations. Analyzes satellite-based water quality assessment capabilities, challenges in algorithm development, and validation requirements. Discusses applications in detecting algal blooms, turbidity, and water quality changes in lakes, reservoirs, and rivers. | 🔗 |
| Salas et al. (2020) | An open-data open-model framework for hydrological models' integration, evaluation and application | Promotes transparent, reproducible hydrological modeling using publicly accessible data and models. Facilitates collaboration, model comparison, and improved water resource predictions through open science practices. | 🔗 |
| Sun and Scanlon (2019) | How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions | Examines how big data and machine learning benefit environment and water management for sustainable development. Reviews applications in drought monitoring, water quality prediction, and resource optimization. Demonstrates potential of data-driven approaches to address water scarcity, quality degradation, and climate change impacts. | 🔗 |
| Thakur et al. (2021) | Smart water conservation through a machine learning and blockchain-enabled decentralized edge computing network | Combines predictive analytics for water demand forecasting with blockchain for secure, transparent water trading and allocation. Supports sustainable water use through intelligent conservation strategies and trusted water markets. | 🔗 |
| Tran et al. (2020) | Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources | Leverages publicly available datasets and ML algorithms to identify flood-prone areas in cities. Supports urban planning, infrastructure development, and emergency preparedness by mapping and forecasting waterlogging hazards. | 🔗 |
| UNESCO and Ziesche (2023) | Open data for AI: what now? | Explores open data for AI applications emphasizing data accessibility, quality, and ethical use. Discusses FAIR principles and importance of open data ecosystems for advancing AI solutions in water resources and sustainable development. Addresses governance, standards, and capacity building for open data initiatives. | 🔗 |
| Wang et al. (2024) | Digital Twins for Wastewater Treatment: A Technical Review | Examines virtual modeling of treatment plants for process optimization, energy efficiency, and operational improvements. Digital twin enables simulation of treatment scenarios, predictive maintenance, and real-time decision support for wastewater management. | 🔗 |
| Xia et al. (2022) | A Framework of Blockchain Technology in Intelligent Water Management | Proposes architecture for reliable and secure water resource data storage, efficient information transmission, and traceable water quality problem-solving. Blockchain creates trust mechanism among stakeholders in water resource utilization and management processes. | 🔗 |
| Xie et al. (2024) | Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management | Uses big data analytics to predict wastewater generation patterns and identify sources of hazardous contaminants. Supports targeted pollution control, regulatory enforcement, and evidence-based water quality protection strategies. | 🔗 |