The role of deep learning in urban water management: A critical review

Article Construction & Building Technology

Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic

Jorge E. Pesantez et al.

Summary: This research utilizes digital twin technology to assess the impact of infrastructure due to changes in water demands associated with the COVID-19 pandemic, providing valuable analyses and insights for water utility managers.

SUSTAINABLE CITIES AND SOCIETY (2022)

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Article Water Resources

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

Charuleka Varadharajan et al.

Summary: The global decline in water quality in rivers and streams has created an urgent need for new watershed management strategies. Machine learning can aid in developing more accurate, computationally tractable, and scalable models for analyzing and predicting river water quality. When combined with decades of process understanding, machine learning has the potential to address water quality problems effectively.

HYDROLOGICAL PROCESSES (2022)

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Article Engineering, Environmental

Interpretable tree-based ensemble model for predicting beach water quality

Lingbo Li et al.

Summary: Tree-based machine learning models offer low-cost and timely solutions for predicting microbial fecal contamination in beach water. However, many of these models are difficult to interpret. This study evaluates five tree-based models and employs the SHAP explanation method to improve interpretability. LightGBM and XGBoost achieve the highest precision and recall scores in predicting Escherichia coli concentration in beach water.

WATER RESEARCH (2022)

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Article Environmental Sciences

Developing stacking ensemble models for multivariate contamination detection in water distribution systems

Zilin Li et al.

Summary: This study presents a new stacking ensemble model that uses multiple water quality parameters for contamination event detection. The proposed method outperforms an artificial neural network (ANN) benchmark method in terms of detection accuracy. The results demonstrate the great potential of the stacking method for detecting contamination events in water distribution systems.

SCIENCE OF THE TOTAL ENVIRONMENT (2022)

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Article Green & Sustainable Science & Technology

Novel leakage detection and water loss management of urban water supply network using multiscale neural networks

Xuan Hu et al.

Summary: A novel leakage detection model based on DBSCAN-MFCN is proposed in this study, which improves the accuracy of leakage detection, enhances detection efficiency, and reduces water loss by reducing the number of categories and optimizing learning labels.

JOURNAL OF CLEANER PRODUCTION (2021)

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Article Computer Science, Interdisciplinary Applications

Flood depth mapping in street photos with image processing and deep neural networks

Bahareh Alizadeh Kharazi et al.

Summary: This study presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads, coupled with edge detection and transform algorithms to estimate floodwater depth. The method demonstrates the potential to provide a simple, reliable, and scalable solution for estimating floodwater depth in flood-prone regions.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2021)

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Article Computer Science, Interdisciplinary Applications

Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

Benjamin D. Bowes et al.

Summary: This research explores the use of reinforcement learning (RL) to create control policies in a virtual urban catchment model, successfully reducing flood risk and comparing different control strategies. RL can proactively manage pond levels based on current and forecast conditions, reducing flood events effectively.

JOURNAL OF HYDROINFORMATICS (2021)

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Article Construction & Building Technology

Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning

Mingzhu Wang et al.

Summary: This study proposes a framework based on deep learning defect detection and metric learning for tracking multiple sewer defects in CCTV videos. Experimental results show that tracking performance can be influenced by detection accuracy and configurations of the metric learning module.

AUTOMATION IN CONSTRUCTION (2021)

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Article Computer Science, Interdisciplinary Applications

Pipedream: An interactive digital twin model for natural and urban drainage systems

Matthew Bartos et al.

Summary: This study introduces a novel digital twin model for water resources management, which integrates sensor data and online models to real-time model water systems and control system dynamics effectively; Using real-world watershed data, it is found that the model can accurately interpolate hydraulic states and predict future states; By providing a complete real-time hydraulic view, it helps to rapidly detect floods, improve maintenance, and control water systems.

ENVIRONMENTAL MODELLING & SOFTWARE (2021)

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Article Engineering, Multidisciplinary

A steerable pyramid autoencoder based framework for anomaly frame detection of water pipeline CCTV inspection

Yutong Jiao et al.

Summary: This study focuses on identifying anomaly frames in CCTV videos to improve pipeline condition assessment accuracy and efficiency, proposing a framework based on SPAE that achieves significant performance improvement. By allowing inspectors to focus more on anomaly frames, the quality of pipeline assessment is enhanced.

MEASUREMENT (2021)

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Article Environmental Sciences

Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks

Zifeng Guo et al.

Summary: The paper presents a data-driven approach utilizing deep neural networks for flood prediction, reducing prediction time to 0.5% compared to physically-based models, with high accuracy and generalizability for various flood analysis applications.

JOURNAL OF FLOOD RISK MANAGEMENT (2021)

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Article Engineering, Civil

U-FLOOD - Topographic deep learning for predicting urban pluvial flood water depth

Roland Loewe et al.

Summary: This study investigates how deep learning can optimize the prediction of 2D maximum water depth maps in urban pluvial flood events by training a neural network model to exploit patterns in hyetographs and topographical data. The U-NET architecture is adapted for this purpose, and a model with 28,000,000 parameters is found to be optimal. U-FLOOD demonstrates similar predictive performance to existing screening approaches for natural rain events and unknown locations, generating flood predictions within seconds.

JOURNAL OF HYDROLOGY (2021)

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Article Engineering, Civil

Leakage Detection in Water Distribution Systems Based on Time-Frequency Convolutional Neural Network

Guancheng Guo et al.

Summary: A leakage spectrogram and TFCNN model were proposed for leakage signal identification in this study, which showed better performance compared to other classification models under different signal-to-noise ratio conditions, with a detection accuracy of 90% even under -10 dB SNR.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2021)

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Review Engineering, Electrical & Electronic

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Wojciech Samek et al.

Summary: With the increasing demand for explainable artificial intelligence (XAI) due to the successful usage of machine learning, particularly deep neural networks, this work aims to provide an overview of the field, test interpretability algorithms, and demonstrate successful usage in application scenarios.

PROCEEDINGS OF THE IEEE (2021)

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Article Engineering, Civil

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Rocco Palmitessa et al.

Summary: This study investigates the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. By applying these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark, it was observed that LSTM predictions generally displayed large variability across training runs. Adding information on the past did not improve the prediction accuracy when the most recent observations were known, but LSTM networks were capable of compensating for missing information in antecedent water depth observations by using other available input features. LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors.

JOURNAL OF HYDRO-ENVIRONMENT RESEARCH (2021)

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Review Geosciences, Multidisciplinary

How computer vision can facilitate flood management: A systematic review

Umair Iqbal et al.

Summary: Better prediction and monitoring of flood events are crucial for reducing their impact on local communities and infrastructure assets, with computer vision playing an increasing role in flood monitoring. However, there is a lack of systemic analysis on the adequacy of computer vision to specific needs in flood management phases, highlighting the need for further research in this domain.

INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION (2021)

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Article Automation & Control Systems

Domain Knowledge-Based Deep-Broad Learning Framework for Fault Diagnosis

Jian Feng et al.

Summary: The article introduces a novel domain-knowledge-based deep-broad learning framework that shows high flexibility and requires few labeled samples. Experimental results demonstrate that the framework performs significantly well in fault diagnosis tasks.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Article Computer Science, Artificial Intelligence

A survey on incorporating domain knowledge into deep learning for medical image analysis

Xiaozheng Xie et al.

Summary: In the field of medical image analysis, researchers are seeking external information beyond current medical datasets to address the small size issue. They integrate domain knowledge from medical doctors into deep learning models, mimicking diagnostic patterns and features of doctors, and applying them to tasks such as disease diagnosis and organ segmentation.

MEDICAL IMAGE ANALYSIS (2021)

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Article Construction & Building Technology

Towards an automated condition assessment framework of underground sewer pipes based on closed-circuit television (CCTV) images

Mingzhu Wang et al.

Summary: This study proposes a framework for evaluating defect severity and sewer condition automatically from CCTV images using computer vision methods, which includes pipe joint detection, defect detection, and the evaluation of defect severity and sewer condition. The framework performs well in defect detection and accurately evaluates Operation and Maintenance defects with an average deviation of 3.06%. It can assist in the review of inspection videos and lays the foundation for fully automated sewer assessment and maintenance planning.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2021)

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Editorial Material Environmental Sciences

What Role Does Hydrological Science Play in the Age of Machine Learning?

Grey S. Nearing et al.

Summary: This paper is derived from a keynote talk given at Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation show that there is more information in large-scale hydrological data sets than previously thought. The paper calls for the hydrology community to focus on developing a quantitative understanding of the value of hydrological process understanding in a modeling discipline increasingly dominated by machine learning.

WATER RESOURCES RESEARCH (2021)

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Article Environmental Sciences

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Kehua Chen et al.

Summary: This study used multi-agent deep reinforcement learning (MADRL) to simultaneously optimize dissolved oxygen and chemical dosage in WWTPs. The optimization based on life cycle perspective showed lower environmental impacts compared to the baseline, while a cost-oriented control strategy exhibited comparable overall performance to the LCA-driven strategy. Retrofitting of WWTPs should consider impact transfer, and future studies need to identify major contributors of each indicator.

CHEMOSPHERE (2021)

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Article Computer Science, Artificial Intelligence

Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting

Baigang Du et al.

Summary: Accurate urban water demand forecasting is crucial for building intelligent water supplying systems and smart cities. The proposed hybrid DWT-PCA-LSTM model effectively handles complex patterns and noise in water demand series, outperforming other benchmark predictive models in predicting both peaks and average water demand with satisfactory performance.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Engineering, Civil

Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation

Wei Xu et al.

Summary: This study introduces a new deep reinforcement learning (DRL) framework for optimal operation of hydropower reservoir systems, which combines Q-learning and two deep artificial neural networks for improved interpretability and performance. Through analysis of knowledge forms and DRL learning parameters, as well as testing on the Huanren hydropower system, the study reveals the impacts of discretization levels on operation performance and the advantages of the DRL approach over benchmark algorithms.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2021)

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Article Engineering, Environmental

Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations

Antonio M. Moreno-Rodenas et al.

Summary: This study introduced a low-cost camera system for high-frequency monitoring of FOG layer dynamics in wastewater pumping stations, using deep learning computer vision to process optical imagery. The technology aids in monitoring pump station operations, providing data support for managing FOG accumulation and transport processes.

WATER RESEARCH (2021)

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Article Environmental Sciences

Machine learning for hydrologic sciences: An introductory overview

Tianfang Xu et al.

Summary: In recent years, the hydrologic community has shown a growing interest in machine learning, driven by the expansion of hydrologic data repositories and the success of machine learning in academic and commercial applications. This overview introduces commonly used machine learning algorithms and deep learning architectures in a non-technical manner, with a focus on applications in hydrologic sciences. The challenges of extrapolating robustness, physical interpretability, and small sample size in hydrologic applications are also highlighted.

WILEY INTERDISCIPLINARY REVIEWS-WATER (2021)

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Article Computer Science, Information Systems

A Pipeline Leak Detection and Localization Approach Based on Ensemble TL1DCNN

Mengfei Zhou et al.

Summary: This paper proposes a novel ensemble transfer learning one-dimension convolutional neural network (TL1DCNN) for pipeline leak detection and localization, which outperforms traditional methods in performance through optimizing ensemble weights and deep learning methods.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data

Leila Hashemi-Beni et al.

Summary: The article introduces an integrated CNN and region growing method for mapping both visible and underneath vegetation flooded areas. Data augmentation is used to enhance the accuracy of image classification, and the proposed integrated method efficiently detects floods in both visible and vegetation-covered areas to support effective flood emergency response and recovery activities.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Article Computer Science, Interdisciplinary Applications

A unified convolutional neural network integrated with conditional random field for pipe defect segmentation

Mingzhu Wang et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2020)

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Article Construction & Building Technology

A deep learning-based framework for an automated defect detection system for sewer pipes

Xianfei Yin et al.

AUTOMATION IN CONSTRUCTION (2020)

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Article Computer Science, Interdisciplinary Applications

Deep Learning-Based Automated Detection of Sewer Defects in CCTV Videos

Srinath Shiv Kumar et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2020)

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Article Computer Science, Interdisciplinary Applications

Detecting Leaks in Water Distribution Pipes Using a Deep Autoencoder and Hydroacoustic Spectrograms

Roya A. Cody et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2020)

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Article Engineering, Civil

Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms

Rong Tang et al.

WATER RESOURCES MANAGEMENT (2020)

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Review Multidisciplinary Sciences

The role of artificial intelligence in achieving the Sustainable Development Goals

Ricardo Vinuesa et al.

NATURE COMMUNICATIONS (2020)

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Article Engineering, Environmental

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution

Manel Garrido-Baserba et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)

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A Review of Cybersecurity Incidents in the Water Sector

Amin Hassanzadeh et al.

JOURNAL OF ENVIRONMENTAL ENGINEERING (2020)

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Article Water Resources

Deep reinforcement learning for the real time control of stormwater systems

Abhiram Mullapudi et al.

ADVANCES IN WATER RESOURCES (2020)

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Burst Detection in District Metering Areas Using Deep Learning Method

Xiaoting Wang et al.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2020)

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Article Construction & Building Technology

Leakage detection in water pipelines using supervised classification of acceleration signals

Harshit Shukla et al.

AUTOMATION IN CONSTRUCTION (2020)

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Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks

Javier Rodriguez-Perez et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)

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Article Water Resources

Using long short-term memory networks for river flow prediction

Wei Xu et al.

HYDROLOGY RESEARCH (2020)

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Article Engineering, Civil

Automated Anomaly Detection and Localization in Sewer Inspection Videos Using Proportional Data Modeling and Deep Learning-Based Text Recognition

Saeed Moradi et al.

JOURNAL OF INFRASTRUCTURE SYSTEMS (2020)

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Article Engineering, Civil

Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

Li Mu et al.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2020)

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Article Engineering, Environmental

Strategic planning of the integrated urban wastewater system using adaptation pathways

Seyed M. K. Sadr et al.

WATER RESEARCH (2020)

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Review Engineering, Environmental

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Jean-David Therrien et al.

WATER SCIENCE AND TECHNOLOGY (2020)

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Preconditioning Water Distribution Network Optimization with Head Loss-Based Design Method

Haixing Liu et al.

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2020)

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A deep convolutional neural network model for rapid prediction of fluvial flood inundation

Syed Kabir et al.

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Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal

Masoumeh Rahimi et al.

INFORMATICS-BASEL (2020)

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Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems

Gergely Hajgato et al.

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Automatic sewer pipe defect semantic segmentation based on improved U-Net

Gang Pan et al.

AUTOMATION IN CONSTRUCTION (2020)

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A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

Ahmed Abdel Nasser et al.

IEEE ACCESS (2020)

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Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

Tuoyuan Cheng et al.

IEEE ACCESS (2020)

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Cyberattack Detection Using Deep Generative Models with Variational Inference

Sarin E. Chandy et al.

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Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event

Liping Yang et al.

SOFT COMPUTING (2019)

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Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification

Duanshun Li et al.

AUTOMATION IN CONSTRUCTION (2019)

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Article Environmental Sciences

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

Catherine Leigh et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2019)

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Review Engineering, Environmental

How Urban Storm- and Wastewater Management Prepares for Emerging Opportunities and Threats: Digital Transformation, Ubiquitous Sensing, New Data Sources, and Beyond - A Horizon Scan

Frank Blumensaat et al.

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A survey of machine learning methods applied to anomaly detection on drinking-water quality data

Eustace M. Dogo et al.

URBAN WATER JOURNAL (2019)

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Detection of multiple leakage points in water distribution networks based on convolutional neural networks

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A defect classification methodology for sewer image sets with convolutional neural networks

Dirk Meijer et al.

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Data-driven predictive energy optimization in a wastewater pumping station

Jorge Filipe et al.

APPLIED ENERGY (2019)

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Underground sewer pipe condition assessment based on convolutional neural networks

Syed Ibrahim Hassan et al.

AUTOMATION IN CONSTRUCTION (2019)

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Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning

Qian Xie et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2019)

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Comparison of learning-based wastewater flow prediction methodologies for smart sewer management

Hazhar Sufi Karimi et al.

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Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring

Abdelkader Dairi et al.

SUSTAINABLE CITIES AND SOCIETY (2019)

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Deep learning identifies accurate burst locations in water distribution networks

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Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

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A deep learning based dynamic COD prediction model for urban sewage

Zifei Wang et al.

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Leak Detection and Location Based on ISLMD and CNN in a Pipeline

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IEEE ACCESS (2019)

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Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer

Duo Zhang et al.

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Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks

Srinath S. Kumar et al.

AUTOMATION IN CONSTRUCTION (2018)

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Article Automation & Control Systems

Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

Jiheon Kang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

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Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks

Riccardo Taormina et al.

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Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT (2018)

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Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants

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Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network

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Big Data Approaches for coastal flood risk assessment and emergency response

James A. Pollard et al.

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Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks

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Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems

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Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

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Short-Term Water Demand Forecast Based on Deep Learning Method

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Optimized Deep Learning Framework for water Distribution Data-Driven Modeling

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The role of deep learning in urban water management: A critical review

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