Research graph
References from Assessing supervised machine learning practice in urban water networks: A critical review of methodological transparency, reproducibility and reporting. Local targets link to admitted publications; unresolved targets remain external evidence.
Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN
10.1016/j.jhydrol.2022.128463 · 2022 · External reference
Applications of machine learning to water resources management: a review of present status and future opportunities
10.1016/j.jclepro.2024.140715 · 2024 · External reference
Effect of data scaling methods on machine learning algorithms and model performance
10.3390/technologies9030052 · 2021 · External reference
Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence
10.1016/j.inffus.2023.101805 · 2023 · External reference
Battle of water demand forecasting
10.1061/jwrmd5.wreng-6887 · 2025 · External reference
Don’t push the button! exploring data leakage risks in machine learning and transfer learning
10.1007/s10462-025-11326-3 · 2025 · External reference
Machine learning-based Monte Carlo hyperparameter optimization for THMs prediction in urban water distribution networks
10.1016/j.jwpe.2025.107683 · 2025 · External reference
Semisupervised clustering approach for pipe failure prediction with imbalanced data set
10.1061/jwrmd5.wreng-6263 · 2024 · External reference
Principles and practice of explainable machine learning
10.3389/fdata.2021.688969 · 2021 · External reference
A survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging
10.3390/jimaging10100239 · 2024 · External reference
Hyperparameter optimization: foundations, algorithms, best practices, and open challenges
2023 · External reference
mlr: machine learning in R. J. Mach
2016 · External reference
The UWO dataset – long-term observations from a full-scale field laboratory to better understand urban hydrology at small spatio-temporal scales
2025 · External reference
Groundwater level forecasting with machine learning models: a review
10.1016/j.watres.2024.121249 · 2024 · External reference
A new typology design of performance metrics to measure errors in machine learning regression algorithms
2019 · External reference
Outlier detection
10.1145/3381028 · 2021 · External reference
Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization
10.1016/j.cor.2018.01.013 · 2019 · External reference
Automated machine Learning: the new wave of machine learning
2020 · External reference
From meteorological perturbation mechanisms to sewer methane forecasting: a causal and machine learning approach
10.1021/acsestengg.5c00822 · 2026 · External reference
Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution
10.1016/j.jhydrol.2022.127440 · 2022 · External reference
SHAP-powered insights into spatiotemporal effects: unlocking explainable bayesian-neural-network urban flood forecasting
2024 · External reference
A systematic review of the limitations and associated opportunities of ChatGPT
2024 · External reference
Real time control of water distribution networks: a state-of-the-art review
10.1016/j.watres.2019.06.025 · 2019 · External reference
Deep learning-based sewer defect classification for highly imbalanced dataset
10.1016/j.cie.2021.107630 · 2021 · External reference
A survey of water utilities’ digital transformation: drivers, impacts, and enabling technologies
10.1038/s41545-023-00265-7 · 2023 · External reference
Evaluating the generalizability and transferability of water distribution deterioration models
10.1016/j.ress.2023.109611 · 2024 · External reference
A comprehensive survey on feature selection in the various fields of machine learning
10.1007/s10489-021-02550-9 · 2022 · External reference
Artificial intelligence in the water domain: opportunities for responsible use
2021 · External reference
Explainable artificial intelligence: a survey
2018 · External reference
Explainable AI (XAI): core ideas, techniques, and solutions
10.1145/3561048 · 2023 · External reference
Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes
10.1016/j.ijsrc.2019.08.005 · 2020 · External reference
Pitfalls in training and validation of deep learning systems
10.1016/j.bpg.2020.101712 · 2021 · External reference
A survey on missing data in machine learning
10.1186/s40537-021-00516-9 · 2021 · External reference
Unresolved reference
External reference
A structured review of literature on uncertainty in machine learning & deep learning
2024 · External reference
Machine learning based water pipe failure prediction: the effects of engineering, geology, climate and socio-economic factors
10.1016/j.ress.2021.108185 · 2022 · External reference
Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks
10.1016/j.ress.2023.109088 · 2023 · External reference
A hardware-in-the-loop water distribution testbed dataset for cyber-physical security testing
10.1109/access.2021.3109465 · 2021 · External reference
The role of deep learning in urban water management: a critical review
10.1016/j.watres.2022.118973 · 2022 · External reference
Evaluation of graph neural networks for urban drainage metamodeling: key components and transferability analysis
10.1016/j.watres.2025.125079 · 2026 · External reference
Unresolved reference
2022 · External reference
Short-term water demand forecast based on deep learning method
10.1061/(asce)wr.1943-5452.0000992 · 2018 · External reference
Sewer-ML: a multi-label sewer defect classification dataset and benchmark
2021 · External reference
Future global urban water scarcity and potential solutions
10.1038/s41467-021-25026-3 · 2021 · External reference
Artificial intelligence policy worldwide: a comparative analysis
10.1098/rsos.242234 · 2026 · External reference
Unresolved reference
External reference
Knowledge-data fusion for water supply pipe failure prediction: a hybrid physics-informed and data-driven method
10.1016/j.ress.2026.112263 · 2026 · External reference
Are all data useful? Inferring causality to predict flows across sewer and drainage systems using directed information and boosted regression trees
10.1016/j.watres.2018.09.009 · 2018 · External reference
Machine learning in natural and engineered water systems
10.1016/j.watres.2021.117666 · 2021 · External reference
On the failings of Shapley values for explainability
10.1016/j.ijar.2023.109112 · 2024 · External reference
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
10.1007/s10994-021-05946-3 · 2021 · External reference
Explainable artificial intelligence for sustainable urban water systems engineering
10.1016/j.rineng.2025.104349 · 2025 · External reference
Improving urban water demand forecast using conformal prediction-based hybrid machine learning models
10.1016/j.jwpe.2023.104721 · 2024 · External reference
A review of missing data handling techniques for machine learning
2022 · External reference
Exploration of deep learning leak detection model across multiple smart water distribution systems: detectable leak sizes with AMI meters
10.1016/j.wroa.2025.100332 · 2025 · External reference
Toward transparent and reproducible machine learning–Based research in Urban water networks
10.1061/jwrmd5.wreng-6868 · 2025 · External reference
Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems
10.1109/tie.2017.2764861 · 2018 · External reference
Machine learning applications for anomaly detection in Smart Water Metering Networks: a systematic review
10.1016/j.pce.2024.103558 · 2024 · External reference
Leakage and the reproducibility crisis in machine-learning-based science
2023 · External reference
Multi-objective hyperparameter optimization in machine learning—An overview
10.1145/3610536 · 2023 · External reference
Leakage in data mining
10.1145/2382577.2382579 · 2012 · External reference
Short term water demand forecast modelling using artificial intelligence for smart water management
10.1016/j.scs.2023.104610 · 2023 · External reference
An interpretable machine learning-based pitting corrosion depth prediction model for steel drinking water pipelines
10.1016/j.psep.2024.08.038 · 2024 · External reference
A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar
2025 · External reference
Applications of machine learning in drinking water quality management: a critical review on water distribution system
10.1016/j.jclepro.2024.144171 · 2024 · External reference
Developing stacking ensemble models for multivariate contamination detection in water distribution systems
10.1016/j.scitotenv.2022.154284 · 2022 · External reference
Artificial intelligence-incorporated prediction for urban flooding processes in the past 20 years: a critical review
10.1016/j.envsoft.2025.106525 · 2025 · External reference
FeatureX: an explainable feature selection for deep learning
10.1016/j.eswa.2025.127675 · 2025 · External reference
Are we learning yet? A meta review of evaluation failures across machine learning
2021 · External reference
Avoiding common machine learning pitfalls
2024 · External reference
Standards, frameworks, and legislation for artificial intelligence (AI) transparency
10.1007/s43681-025-00661-4 · 2025 · External reference
Explainable artificial intelligence for reliable water demand forecasting to increase trust in predictions
2025 · External reference
Tuning ANN hyperparameters for forecasting drinking water demand
10.3390/app11094290 · 2021 · External reference
Predicting non-deposition sediment transport in sewer pipes using random forest
10.1016/j.watres.2020.116639 · 2021 · External reference
A review of evaluation metrics in machine learning algorithms
2023 · External reference
Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes
10.1007/s00477-023-02565-3 · 2025 · External reference
The Bellinge data set: open data and models for community-wide urban drainage systems research
10.5194/essd-13-4779-2021 · 2021 · External reference
A critical review of short-term water demand forecasting tools—What method should I use?
10.3390/su14095412 · 2022 · External reference
Towards a smart water city: a comprehensive review of applications, data requirements, and communication technologies for integrated management
10.1016/j.scs.2021.103442 · 2022 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
2021 · External reference
Could ChatGPT automate water network clustering? A performance assessment across algorithms
10.3390/w17202995 · 2025 · External reference
Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining
10.1016/j.watres.2023.120791 · 2023 · External reference
The impact of feature scaling in machine learning: effects on regression and classification tasks
2025 · External reference
Investigation of performance metrics in regression analysis and machine learning-based prediction models
2022 · External reference
A review of feature selection methods for machine learning-based disease risk prediction
10.3389/fbinf.2022.927312 · 2022 · External reference
Adoption of artificial intelligence in drinking water operations: a survey of progress in the United States
10.1061/jwrmd5.wreng-5870 · 2023 · External reference
Machine learning in Python: main developments and technology trends in data science, Machine learning, and artificial intelligence
10.3390/info11040193 · 2020 · External reference
Leveraging deep learning and language models in revolutionizing water resource management, research, and policy making: a case for ChatGPT
10.1021/acsestwater.3c00264 · 2023 · External reference
Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning
10.1038/s41598-025-90612-0 · 2025 · External reference
Rewards, risks and responsible deployment of artificial intelligence in water systems
10.1038/s44221-023-00069-6 · 2023 · External reference
Exploring spatial and temporal importance of input features and the explainability of machine learning-based modelling of water distribution systems
10.1016/j.dche.2024.100202 · 2025 · External reference
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
10.1038/s42256-021-00307-0 · 2021 · External reference
Prediction of pipe failures in water supply networks using logistic regression and support vector classification
10.1016/j.ress.2019.106754 · 2020 · External reference
Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes
10.2166/wst.2019.106 · 2019 · External reference
AutoML: a systematic review on automated machine learning with neural architecture search
2024 · External reference
Making waves: the potential of generative AI in water utility operations
10.1016/j.watres.2024.122935 · 2025 · External reference
A critical overview of outlier detection methods
10.1016/j.cosrev.2020.100306 · 2020 · External reference
Improving urban water security through pipe-break prediction models: machine learning or survival analysis
10.1061/(asce)ee.1943-7870.0001657 · 2020 · External reference
Integrated intelligent models for predicting water pipe failure probability
10.1016/j.aej.2023.11.047 · 2024 · External reference
Battle of the attack detection algorithms: disclosing cyber attacks on water distribution networks
10.1061/(asce)wr.1943-5452.0000969 · 2018 · External reference
Does ChatGPT ignore article retractions and other reliability concerns?
10.1002/leap.2018 · 2025 · External reference
Enhancing urban flood susceptibility assessment by capturing the features of the urban environment
10.3390/rs17081347 · 2025 · External reference
Improving the leak detection efficiency in water distribution networks using noise loggers
10.1016/j.scitotenv.2022.153530 · 2022 · External reference
Machine learning algorithm validation with a limited sample size
10.1371/journal.pone.0224365 · 2019 · External reference
Battle of the leakage detection and isolation methods
10.1061/(asce)wr.1943-5452.0001601 · 2022 · External reference
Leveraging large language models for automating water distribution network optimization
2026 · External reference
Review of classification methods on unbalanced data sets
10.1109/access.2021.3074243 · 2021 · External reference
Friends don't let friends use Nash-Sutcliffe Efficiency (NSE) or KGE for hydrologic model accuracy evaluation: a rant with data and suggestions for better practice
10.1016/j.envsoft.2025.106665 · 2025 · External reference
Graph Neural networks for State estimation in Water Distribution systems: application of supervised and semisupervised learning
10.1061/(asce)wr.1943-5452.0001550 · 2022 · External reference
Interpretable deep learning for acoustic leak detection in water distribution systems
10.1016/j.watres.2024.123076 · 2025 · External reference
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
10.5194/hess-25-5839-2021 · 2021 · External reference
Urban drainage system planning and design - challenges with climate change and urbanization: a review
10.2166/wst.2015.207 · 2015 · External reference
Unresolved reference
External reference
Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system
10.1016/j.scitotenv.2024.175573 · 2024 · External reference
Sweating the assets - the role of instrumentation, control and automation in urban water systems
10.1016/j.watres.2019.02.034 · 2019 · External reference
How does missing data imputation affect the forecasting of urban water demand?
10.1061/(asce)wr.1943-5452.0001624 · 2022 · External reference
Explainable and causal machine learning to investigate the spatiotemporal dynamics patterns of coastal water quality in Hong Kong
10.1016/j.watres.2025.125026 · 2026 · External reference
Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model
10.1016/j.jclepro.2022.131724 · 2022 · External reference
Impacts of building configurations on urban stormwater management at a block scale using
2022 · External reference
Risk-driven composition decoupling analysis for urban flooding prediction in high-density urban areas using Bayesian-Optimized LightGBM
10.1016/j.jclepro.2024.142286 · 2024 · External reference
Machine learning in environmental research: common pitfalls and Best practices
10.1021/acs.est.3c00026 · 2023 · External reference
A review of the application of machine learning in water quality evaluation
10.1016/j.eehl.2022.06.001 · 2022 · External reference
Ensemble data mining modeling in corrosion of concrete sewer: a comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models
10.1016/j.aei.2019.101030 · 2020 · External reference
