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.
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,…
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data…
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.
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.
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…
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.
In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water p…
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.
Water balance models (WBMs) are often employed to understand regional hydrologic cycles over various time scales. Most WBMs, however, are physically-based, and few employ state-of-the-art statistical methods to reconcile independent input measurement uncertainty and bias. Further, few WBMs exist for large lakes, and mo…
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.
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…
When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamina…
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.
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.
The paper develops a new framework for managing asymmetric volatility.
problem Managing asymmetric volatility to improve recovery and participation.
method Path-dependent framework for asymmetric volatility management.
result Skew engineering reduces harmful downside participation more than productive upside participation.
Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimized combinations of steady-state (SS) scans for precisely estimating the fast-relaxing signal fraction ff of a two-compartment signal model, …
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.
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.
As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation. According to previous studies, device level consumption could lead to significant freshwater conservation. Existing water disaggregation methods focus on learning the signatures for appliances; how…
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.
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.
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.
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.
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.
Modeling influenza spread using feature engineering and international flow deconvolution.
problem Predicting and mitigating influenza spread through feature extraction and international flow analysis.
method Discrete Fourier Transform, matrix completion, SVM, autoencoders, PCA, deconvolution of international flow.
result Significant environmental and economic features are crucial to influenza mortality.
Low-cost water-level tracking using LTE power metrics and wavelet analysis.
problem Real-time water-level monitoring across many locations with fixed instruments.
method Extracts per-antenna RSRP, RSSI, and RSRQ, applies CWT to RSRP, and uses a neural network to track water-level changes.
result Achieves root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively, under line-of-sight conditions.
Generative model improves noise estimation in stochastic rotating shallow water models.
problem Improving noise estimation in stochastic partial differential equations for fluid dynamics.
method Replaced PCA with a generative model to avoid constraints on stochastic increments.
result Generative model produces better RMSE, CRPS score, and forecast rank histograms.
TCA algorithm saves up to 40% energy in IoT devices.
problem Energy consumption and user comfort trade-off in IoT devices.
method Incremental learning Temporal Clustering Algorithm (TCA) for IoT devices.
result Achieved up to 40% energy savings with high success rate.
Novel Orlicz regrets consistently bound environmental variable statistics.
problem Consistent evaluation of stochastic environmental variables like water quality indices.
method Proposed novel Orlicz regrets for upper and lower bounds.
result Explicit linkage between Orlicz regrets and divergence risk measures.
The paper presents anomaly detection in time series data using InfluxDB and Python.
problem Anomalous data points in time series data affect decision making in water and environmental systems.
method Data cleaning, cost-sensitive machine learning (Logistic Regression, Random Forest, SVM), feature selection, and InfluxDB integration.
result Random Forest outperformed other models in detecting anomalies.
The study uses Gaussian mixture models to estimate pipe wall thickness from partial scans.
problem Estimating remaining wall thickness of critical water mains in non-destructive evaluation.
method Developed a robotic vehicle for pipe inspections. Used Gaussian Processes and Gaussian Mixture Models to infer RWT at unseen sections.
result Gaussian mixture models effectively capture the probability of RWT values in inspected data.
The study analyzes river water quality using statistical and machine learning methods.
problem Analyzing spatio-temporal dynamics of dissolved oxygen in the River Thames.
method Superstatistical methods and machine learning (e.g., Light Gradient Boosting Machine, Informer model).
result The Informer model outperforms others in long-term dissolved oxygen concentration forecasting.
Deep learning method improves myelin water fraction estimation.
problem Estimating myelin water fraction in the brain using magnetic resonance relaxometry.
method Combines input layer regularization with automated regularization hyperparameter tuning.
result Proposed method outperforms classical methods and multi-layer perceptrons on in vivo brain data.
A hybrid physics-ML model predicts FO water flux with high accuracy and uncertainty quantification.
problem Challenges in accurately modeling Forward Osmosis water flux due to complex internal mass transfer phenomena.
method Robust Hybrid Physics-ML framework using Gaussian Process Regression (GPR) for uncertainty-aware Jw prediction.
result Achieved a state-of-the-art MAPE of 0.26% and R2 of 0.999 on independent test data.
Automated neural network potentials achieve coupled cluster accuracy for protonated water clusters.
problem Creating highly accurate potential energy surfaces for chemical systems.
method Automated fitting of neural network potentials to ab initio reference calculations.
result Single potential energy surface for H3O+ to H9O4+ clusters at essentially converged coupled cluster accuracy.
We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water se…
These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and det…
Predictive Q-learning algorithm for IoT networks with human operators.
problem Resilient and predictive actions for IoT networks with faulty components.
method Predictive and resilient Q-learning algorithm considering historical data and human operator feedback.
result Optimal scheduling policies avoiding attacked locations and faults.
Physically-based overland flow models are computationally demanding, hindering their use for real-time applications. Therefore, the development of fast (and reasonably accurate) overland flow models is needed if they are to be used to support flood mitigation decision making. In this study, we investigate the potential…
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple …
Deep learning automates biofouling detection in ship hull images.
problem Automating biofouling assessment from ship hull images.
method Deep learning model trained on expert-annotated images.
result Deep learning model agrees with expert annotations.