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.
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.
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.
Study predicts stream turbidity using surrogate data and meta-model.
problem Costly turbidity sensor deployment limits monitoring networks.
method Dynamic regression (ARIMA), LSTM, GAM models; surrogate covariates (rainfall, water level, temperature, solar exposure); meta-model combining strengths of individual models.
result ARIMA and GAM models with all covariates outperform single models; meta-model yields highest accuracy.
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.
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.
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…
Paper compares modern regression methods for time series data.
problem Regression analysis of time series data with time-indexed predictors.
method Classical statistical and recent machine learning approaches compared.
result Advantages and disadvantages of current methods identified.
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.
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.
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.
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.
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…
Paper proposes ICWT method to balance sewer flow and WWTP capacity.
problem Balancing spatial mismatch of sewer flow and WWTP capacity.
method Hydraulic model and LSTM for multi-step ahead water level prediction.
result LSTM outperforms other models in predicting water levels.
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.
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.
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.
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…
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.
CoCAI uses copulas for accurate multivariate time-series forecasting and anomaly detection.
problem Accurate multivariate time-series forecasting and robust anomaly detection.
method Copula-based conformal prediction for multivariate time-series analysis.
result CoCAI provides statistically valid predictive regions and robust anomaly scores.
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 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.
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.
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.
Meta-learning approach for spatial-temporal prediction across cities.
problem Spatial-temporal prediction for cities with limited data.
method Meta-learning paradigm with spatial-temporal network and pattern-based memory.
result Meta-learning model improves prediction accuracy over multiple tasks.
Study uses deep neural networks for flood forecasting.
problem Accurate flood predictions everywhere.
method Artificial deep neural networks for time-series forecasting.
result Neural networks improve flood predictions.
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.
Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effect…
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 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.
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.
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.
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…
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.
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.
Modeling financial markets as gas molecules, the paper predicts phase transitions similar to water and steam.
problem Understanding the dynamics of financial markets through phase transitions.
method Developed a lattice gas model equivalent to the Ising model on a social network, analyzing critical exponents and auto-correlations.
result Financial market dynamics exhibit phase transition-like behavior, with critical exponents analogous to water and steam.
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…
Proposes a probabilistic model to improve hydrology predictions and trust.
problem Noisy or missing basin characteristics impact streamflow prediction.
method Probabilistic inverse modeling framework to reconstruct basin characteristics.
result 6% improvement in R2 for streamflow prediction, 17% reduction in uncertainty. We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality…
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…
Physics-guided models improve lake temperature and quality predictions.
problem Predicting and monitoring water temperature and quality in lakes.
method Combining physics-based models and recurrent neural networks with physical constraints.
result Improved prediction accuracy and scientific consistency.
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.
Optimizes classification algorithms with bounds on error rates.
problem Bounding uncertainties in classifier outputs for diagnostic testing.
method Set-theoretic and probabilistic arguments to derive uniform error bounds.
result Optimal partition minimizes the largest Gershgorin radius of the confusion matrix.
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.
ML models predict water table depth more accurately than PB models, especially in data-scarce regions.
problem Uncertainty in large-scale simulations of water table depth due to biased observational data and model flexibility.
method Constructed three ML models using XGBoost and over 20 million real and proxy observations across the US and Canada.
result ML models correlate 0.6-0.75 with real and proxy observations, significantly better than PB models (0.21-0.40).
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.