Predicts lead contamination in Flint's water system based on home attributes.
problem Understanding and predicting lead contamination in Flint's water system.
method Data science approach using a large dataset of water tests and a crowd-sourced prediction challenge.
result Elevated lead risks can be weakly predicted from observable home attributes.
Paper proposes a technique to detect and predict sources of contaminants in complex systems.
problem Difficulty in identifying sources of contaminants in coupled natural and human systems.
method Developed a technique for simultaneous source detection and prediction.
result Outperforms other approaches in detecting potential groundwater contamination.
AquaSight detects water impurity using deep learning.
problem Water pollution and lack of affordable water quality assessment.
method Convolutional Neural Networks for automated water impurity detection.
result Deep learning model achieved 96% accuracy in detecting water contamination.
Neural networks compress and sample WDN contamination dynamics efficiently.
problem Infrastructure monitoring of complex, networked systems like water distribution networks is expensive and challenging.
method Developed Graph Fourier Transform (GFT) operators and neural networks (NN) for efficient data collection and inference.
result High accuracy reconstruction of contamination dynamics using only 5-10% of the sample set.
Researchers in Flint use predictive tools to locate lead pipes.
problem Detecting and replacing lead pipes in Flint, Michigan after water contamination.
method Statistical and machine learning approaches, along with government recommendations.
result Developed a statistical model that adapts to new information.
Research develops a water quality prediction model using LSTM.
problem Global degradation of water resources and need for optimal water quality monitoring.
method Developed a multivariate water quality prediction model using LSTM and historical data.
result Multiple step LSTM model achieved RMSE of 0.227 mg/L.
Deep learning enhances water resources management through data analysis.
problem Data volume and variety in water resources management.
method Systematic review of deep learning applications in hydrology and water resources.
result Deep learning improves water resources monitoring, prediction, and classification.
Model predicts climate-sensitive water and electricity use in Midwestern cities.
problem Ensuring conservation measures in growing cities under climate change.
method Statistical learning theory-based modeling framework for predicting climate-sensitive water-electricity demand nexus.
result Water use is slightly more sensitive to climate than electricity use.
Machine learning models estimate water parameters from hyperspectral data.
problem Estimating multiple water parameters from hyperspectral data.
method Regression framework using machine learning models, PCA preprocessing.
result Machine learning models can accurately estimate water parameters from hyperspectral data.
Improved water balance model for large lakes using statistical methods.
problem Uncertainty and bias in independent input measurements for large lake hydrologic cycles.
method Developed a Bayesian statistical water balance model (L2SWBM) for Lakes Superior and Michigan-Huron.
result Demonstrated L2SWBM from 26 alternatives that adequately close the water balance of the lakes.
Reinforcement learning optimizes hot water production with 20% savings.
problem Suboptimal energy efficiency in hot water production systems.
method Deep reinforcement learning for optimal control of hot water systems.
result Reduces energy consumption by 20% with no loss of occupant comfort.
Model forecasts water demand with probabilistic multi-step-ahead approach.
problem Accurate probabilistic forecasts of water demand for operational control.
method Time series model with Lasso for high-dimensional feature space.
result Accurate, interpretable, and fast computable forecasting model.
A model for groundwater trading among stakeholders.
problem Groundwater trading among stakeholders in a basin.
method Optimization of production by agents considering water rights, consumption, and trading.
result Characterization of Nash equilibrium in a 1-period setting and initial insights into multi-period game.
The study forecasts water quality from satellite data using machine learning.
problem Predicting future water quality from satellite data for coastal regions.
method Decomposed time series into components and used machine learning models (SARIMA, regression, neural network).
result Regression and neural network models are best at predicting Chl-a, SARIMA model best at FLH and SST.
Predict water pipe failures using machine learning and survival analysis.
problem Difficulty in accessing water pipes for maintenance.
method Classical and modern classifiers for short-term prediction, survival analysis for long-term forecast, and oversampling technique for imbalanced data.
result Identifies important risk factors for water pipe failures.
Quantile regression improves urban water demand forecasting.
problem Improving probabilistic urban water demand forecasting.
method Comparing five quantile regression algorithms and their combinations for one-day ahead forecasting.
result Linear boosting algorithm performs best for probabilistic urban water demand forecasting.
DESS MRI and kernel learning enable precise myelin water quantification.
problem Quantifying myelin water content in the brain.
method Optimized DESS scans combined with kernel learning for precise estimation of myelin water fraction.
result DESS PERK ff estimates are quantitatively similar to conventional MESE MWF estimates.
Enhances water disaggregation for parallel appliances using shape features and Bayesian Discriminative Sparse Coding.
problem Accurately discriminate and disaggregate water consumption patterns from parallel appliances.
method Bayesian Discriminative Sparse Coding (BDSC-LP) with Laplace Prior, shape features, Gibbs sampling.
result Extensive experiments validate the effectiveness of the proposed model.
Variational autoencoders model water Cherenkov detector data.
problem Modeling generative distribution of water Cherenkov detector data.
method Variational autoencoders and normalizing flows.
result Demonstrated capability of variational autoencoders for generative modelling.
Deep learning aids water science by tackling complex data challenges.
problem Interdisciplinary challenges in water research.
method Application of deep learning techniques to water science problems.
result Deep learning can reveal emergent behaviors of problem-specific units.
The paper develops models to predict the remaining useful life of water pipes.
problem Predicting the remaining useful life of deteriorating water pipes.
method Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference System (ANFIS) applied to field data.
result Models accurately predict the reduction of remaining useful life due to pipe deterioration.
LightGBM outperforms other models in predicting pH values in Georgia, USA.
problem Accurate water quality prediction for effective resource management and pollution mitigation.
method Five distinct predictive models (linear regression, Random Forest, XGBoost, LightGBM, MLP neural network) were assessed for pH value forecasting in Georgia, USA.
result LightGBM achieved the highest average precision in predicting pH values.
Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.
problem Uncertain reservoir conditions lead to unstable gas recovery and low resource efficiency.
method Developed optimal portfolios of well designs based on reservoir conditions and probabilities.
result Remarkable reduction in variation and substantial increase in energy efficiency achieved.
Paper tackles invariance of demodulation in shallow water acoustic communications.
problem Frequency-selective signal distortion (Doppler effect) in shallow water environments.
method Developed ML-based demodulation methods using DBN-NN and DBN-CNN.
result Demonstrated invariance of the proposed method to Doppler effect with 2dB error margin.
Light pillars over rippled water appear parallel due to projection geometry.
problem Light pillars over rippled water appear parallel.
method Developing a geometric optics model
result The phenomenon is explained using the specular reflection rule, projection geometry, and physics of surface slopes.
Machine learning predicts liquid water properties from cluster data.
problem Accuracy of bulk properties from machine-learned potentials is limited by training data.
method Local, atom-centred descriptors enable prediction of bulk properties from cluster data.
result Excellent agreement with experimental and theoretical counterparts of liquid water properties.
Neural network for water treatment anomaly detection with GA architecture optimization.
problem Detect anomalies in water treatment systems.
method Genetic algorithms for NN architecture optimization, NAB metric, F1-metric drawbacks analysis, techniques to improve AD quality.
result Improved anomaly detection quality through genetic algorithms and techniques.
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context,…
Hölder-Bayes robustly infers model parameters and contamination levels.
problem Robustness to data contamination in Bayesian inference.
method Introduces Hölder-Bayes framework for joint inference of model parameters and contamination proportion using Hölder divergence.
result Hölder-Bayes framework provides robust parameter inference, contamination-level recovery, and uncertainty-aware outlier detection.
Study predicts coastal water quality using machine learning, identifying salinity as key factor.
problem Predicting and managing coastal water quality for public health and tourism.
method Machine learning models (Catboost, Xgboost, Random Forests, Support Vector Regression, Artificial Neural Networks) trained on environmental data.
result Catboost algorithm performed best, with R² values of 0.71 and 0.68 for E. Coli and enterococci predictions.
A contamination in a 3-manifold is an object interpolating between the contact structure and the lamination. Contaminations seem to provide a link between 3-dimensional contact geometry and the classical topology of 3-manifolds, as described in a separate paper. In this paper we deal with contaminations carried by bran…
Machine learning models estimate nutrient concentrations from water quality surrogates.
problem Estimating high frequency nutrient concentrations from limited in-situ measurements.
method Used machine learning (Random Forests) to estimate nutrient concentrations using surrogate measures.
result Reduced RMSE by up to 60.1% compared to linear models, with additional sensors not providing significant benefits.
A robust loss for anomaly mitigation and unsupervised contamination classification
problem Detecting and mitigating contamination in supervised and unsupervised settings
method Neural Bayesian Anomaly Mitigation (NBAM)
result Recovering the structure of contamination and identifying label-flip pairs
New model clusters water pollution networks without parametric distribution assumptions.
problem Assessing environmental threat from coal mining via sulfate pollution in river networks.
method Exponential-family random graph models and local likelihood estimation for nonparametric weighted network analysis.
result Proposes scalable method for large-scale environmental studies.
Study minimax robustness in statistical estimation under Wasserstein contamination.
problem Adversarial perturbations in statistical data.
method Developed minimax theory for ℓqr losses under Wasserstein-r contaminations. result Exact minimax risk identified for joint contaminations in location estimation and prediction in linear regression.
New UCB algorithms resist contamination in bandit problems.
problem Stochastic bandit problems with ε-contaminated rewards.
method Robust mean estimators and crUCB algorithms.
result Achieves O(√KTlogT) regret for small contamination proportions.
Study shows robust method for estimating density ratios even with heavy contamination.
problem Estimating density ratios in the presence of heavy contamination.
method Weighted density ratio estimation (DRE) with doubly strong robustness.
result Weighted DRE achieves sparse consistency under heavy contamination.
Generative models can still learn from contaminated data, but with limitations.
problem How much contamination can generative models tolerate?
method Characterized robustness under contaminated enumerations, proving generation is achievable for all countable collections if contamination fraction converges to zero.
result Generation under contamination is achievable for all countable collections if contamination fraction converges to zero, but dense generation is strictly less robust.
Deep learning designs effective preconditioners for water engineering problems.
problem Solving large linear systems in water engineering applications.
method Convolutional Neural Network (CNN) for designing preconditioning matrices.
result Learned preconditioners improve convergence rates beyond existing methods.
Hybrid model predicts flow and pressure in water systems.
problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.
Algorithm identifies Pareto set in bandits with contaminated feedback.
problem Identifying Pareto set in multi-objective bandits with adversarial contamination.
method Sample median-based multi-objective adaptive elimination algorithm.
result Sample complexity bound that depends on contamination probability.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
problem Model bias in traditional physics-based models across different income and education levels.
method Physics-guided GNNs with refined neighbor selection and weights.
result Preserves equitable performance across different sensitive groups in the Delaware River Basin.
Optimal estimation of low-rank matrices from contaminated data.
problem Reconstructing a low-rank matrix from a contaminated version of itself.
method Developed an asymptotically optimal algorithm to estimate the original matrix from the singular values of the contaminated matrix.
result Found an explicit signal-to-noise cutoff below which estimation fails.
Combines machine learning and convex limiting for accurate subgrid flux modeling in shallow-water equations.
problem Accurate subgrid flux modeling in shallow-water equations.
method Machine learning and flux limiting for property-preserving subgrid scale modeling.
result The proposed method produces meaningful closures even in untrained scenarios.
New algorithm resists contamination in high-dimensional regression with optimal performance.
problem Adversarial and measurement errors in high-dimensional data.
method Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm.
result Achieves minimax near-optimal estimation and signal-adaptive support recovery.
Paper introduces RAS for robust MTL with contamination.
problem Representation-based multi-task learning struggles with contamination.
method Robust and Adaptive Spectral (RAS) method.
result RAS prevents negative transfer and performs well with up to 80% contamination.
Paper tackles contaminated bandits, identifying best true distribution with high probability.
problem Identifying the best true distribution in contaminated bandits.
method Developed tight sample complexity bounds for estimating robust moments from contaminated samples, adapting classical algorithms.
result Derived matching information-theoretic lower bounds for sample complexity.
Trimming helps in conformal prediction when it separates anomaly scores.
problem Effectiveness of trimming in conformal prediction under contamination.
method Analyse fixed-threshold trimming as a replacement of the contaminated calibration law with a retained law.
result Trimming helps when it separates anomaly scores, reducing clean-target coverage to a one-dimensional score-CDF transfer problem.