Data Assimilation-Based Real-Time Optimization Framework for Hydraulic Fracturing Pumping Operations

A Forecast-Informed Rule-Based Control Framework for Optimizing the Hydraulic Performance of Bioretention Systems

Bioretention systems are widely implemented to mitigate urban runoff; however, conventional passive designs lack the ability to adapt discharge behavior to evolving rainfall conditions, often resulting in premature drainage and inefficient utilization of available storage. This study develops and evaluates a forecast-informed, rule-based control framework for optimizing the long-term hydraulic performance of bioretention systems. The proposed approach integrates continuous valve actuation with a kernel-based rainfall forecast indicator embedded directly within a process-based hydraulic model implemented in OpenHydroQual. Control parameters governing forecast interpretation and valve responsiveness are optimized offline using a genetic algorithm over multiyear simulations. System performance is evaluated using three complementary metrics: exceedance flow at a probability level of p = 0.001 , total discharged volume, and a residence-time objective, defined as the outlet load of a Laplace-transformed age variable. Simulation-based evaluation using a field-calibrated model of a dual bioretention system demonstrated that exceedance-only optimization reduces extreme discharge from 1.99 × 10 2 to 2.86 × 10 1    m 3 / day (approximately 86%) relative to passive operation. Multiobjective optimization revealed a well-defined Pareto trade-off between exceedance suppression and residence-time enhancement. Increasing the residence-time weight yielded a 52% reduction in the transformed-age load while increasing exceedance by less than 20% relative to the exceedance-only solution. Notably, moderate weighting achieved approximately 40% improvement in residence time with less than 3% increase in exceedance, indicating a region of hydraulically efficient operating conditions. Results demonstrate that forecast-informed rule-based control can substantially improve both extreme-flow mitigation and storage persistence without requiring online optimization or computationally intensive model predictive control. The proposed framework provides a transparent, computationally lightweight, and physically interpretable approach for enhancing the operational performance of existing bioretention infrastructure under long-term, variable hydrologic conditions.

Time Series Decomposition-Based Prediction Model for Sustainable Reservoir Operation and Flood Risk Management in Backwater Reaches

Water level prediction for the backwater reaches of large reservoirs is a critical step for many tasks of reservoir operation and flood control, directly affecting the sustainability of water–energy–ecosystem balance. The problem is very challenging due to arbitrarily complicated hydrodynamic mechanisms and various types of influencing factors. This paper proposes a method based on time series decomposition for feature extraction from data samples by a novel neural architecture. To accurately quantify the complex hydraulic conditions of large reservoirs, we investigate a type of neural basis expansion to incorporate exogenous variables (e.g., reservoir regulation and storage, upstream confluence, and flow travel time). Unlike the traditional LSTM-based methods, our method is free from recurrent architecture. It can exploit backward and forward residual links as a backbone to ensure the validity and structural distribution of the information during the model training. Extensive experiments on real data of the Three Gorges Reservoir are implemented to evaluate the performance of the proposed method. The results show that the proposed method shows state-of-the-art performance on all evaluation metrics and can provide reliable technical support for the refined and sustainable operation of large reservoirs.

Artificial Intelligence for Hydraulic-Fracturing Decision Support: A Workflow-Oriented Critical Review

Hydraulic fracturing is a critical technology for unconventional oil and gas development, but its performance is strongly affected by geological heterogeneity, complex fracture propagation, operational uncertainty, and nonlinear interactions among engineering parameters. Artificial intelligence (AI) provides tools for extracting relationships from geological, geophysical, operational, and production data. This structured narrative review synthesizes AI applications across four sequential stages of the hydraulic-fracturing workflow: sweet-spot identification, fracturing-parameter optimization, operational diagnosis and risk warning, and post-fracturing flowback prediction and control. Representative studies reported sweet-spot classification accuracy of 97.5% and R2 = 0.97 for production-performance prediction; a simulator-coupled optimization study reported a 13% economic improvement, and field-data models used cohorts of up to 295 wells. Operational studies reported point-event recognition above 97%, pressure forecasting 30 s ahead, and risk forecasts over three consecutive 60 s intervals. A post-fracturing model trained on 286 wells predicted responses over 30-, 90-, 180-, and 360-day horizons. These values are study-specific and are not directly comparable because the datasets, targets, partitions, and metrics differ. Collectively, the evidence indicates measurable but uneven progress; field readiness remains limited by data quality, multimodal alignment, physical consistency, uncertainty quantification, external validation, and weak coupling between model outputs and operational decisions. The review contributes a reproducible workflow-oriented coding framework and defines validation and deployment priorities for reliable, interpretable, and executable AI-assisted fracturing decision support.

Performance and Limitations of Machine Learning Models for Groundwater Level Prediction Using Hydro-Climatic Variables

Accurate groundwater level (GWL) prediction is essential for sustainable groundwater management and resource planning. However, it is challenging in heterogeneous hydrogeological settings for physics-based models, particularly under limited subsurface characterisation. Machine learning (ML) techniques can capture complex spatio-temporal groundwater dynamics, complementing conventional modelling approaches. This study systematically evaluates the performance and limitations of ML models for GWL prediction in sandstone and mudstone formations, with emphasis on the influence of local hydrogeological conditions. Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Random Forest (RF), along with their wavelet-enhanced counterparts, were applied to monthly hydro-climatic data from 11 observation wells in the Lower Otter Catchment, UK, covering 2011–2023. The final four years were reserved for validation. Time-series predictors were used because time-invariant and sparsely available geological parameters provide limited explanatory power for local-scale GWL dynamics. Their influence is implicitly reflected in observed GWL responses. Model performance was assessed using statistical criteria, including the coefficient of determination (R 2 ). Additionally, a new metric, the Data Difference and Trend Index (DDTI), was introduced to measure the proportion of simulated values matching observed trends within a predefined threshold (e.g. 0.5 m). Model performance was site-specific, with validation R 2 ranging from < 0.1 to > 0.9. This indicates the dominant influence of local hydrogeological conditions, with lower accuracy observed in partially confined, non-recharge-dominated, and river-disconnected wells. A 2-month time lag produced optimal model performance, reflecting the catchment’s characteristic response time to infiltration processes. Individual models occasionally outperformed ensemble averages, which showed fewer outliers. Wavelet transforms did not consistently enhance performance. Model efficacy varied seasonally, with validation R 2 markedly lower in summer (e.g. < 0.1) and higher in autumn (e.g. > 0.9). This emphasises key limitations of ML-based GWL prediction, including reduced reliability near lithological boundaries and strong sensitivity to hydro-climatic conditions, constraining model transferability. Overall, the findings highlight the value of moving beyond performance benchmarking to explicitly identify hydro-climatic and hydrogeological conditions under which ML models lose reliability, informing groundwater modelling and sustainable water management. Graphical Abstract This study evaluates the performance and limitations of machine learning (ML) models for predicting groundwater levels (GWL) in sandstone and mudstone formations using hydro-climatic variables across 11 observation wells in the Lower River Otter Water Body, UK. Four ML models, i.e. Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Random Forest (RF), along with their wavelet-enhanced versions, were applied to monthly hydro-climatic data from 2011 to 2023, with the last four years reserved for validation. Model performance was assessed using the coefficient of determination (R 2 ), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE), and a novel Data Difference and Trend Index (DDTI), which quantifies the proportion of simulated data following observed trends within a defined threshold. Results indicate that predictive accuracy is highly site-specific, with local hydrogeological conditions strongly influencing outcomes. No model consistently captured GWL near interior boundaries where sandstone is confined by mudstone, and neither wavelet transforms nor model ensembles reliably improved performance. Seasonal variability also affected model efficacy, with the highest accuracy in autumn and the lowest in summer. Overall, the workflow highlights the limitations of ML for GWL prediction and provides insights for future hydrogeological modelling.

A Study on Seepage Pressure Forecasting for Concrete Dams Based on Multi-Scale Preprocessing and Dual-Model Integration

Seepage pressure time series of concrete dams are governed by reservoir water level, rainfall, temperature and long-term aging effects, featured by strong nonstationarity and complex multi-scale fluctuations. Existing decomposition–ensemble methods ignore nonlinear coupling among scale components, suffering low prediction accuracy and poor physical interpretability. Current model fusion schemes fail to adapt to differentiated evolution mechanisms of frequency-varying seepage components and cannot fully mine implicit cross-scale nonlinear correlations. To overcome these drawbacks, this study proposes a concrete dam seepage pressure prediction approach integrating ensemble empirical mode decomposition, multi-scale preprocessing, and optimized dual-model selection combining ridge regression and Transformer–BiLSTM. Ensemble empirical mode decomposition adaptively denoises and decouples raw seepage series into high-, medium- and low-frequency IMFs according to oscillation cycles. A normalized Comprehensive Optimization Index is constructed to parallelly train ridge regression and Transformer–BiLSTM for each component and select the optimal submodel dynamically. A fully connected nonlinear fusion layer reconstructs multi-scale predictions to retain inherent component coupling features, replacing traditional simple linear superposition. Engineering cases verify that the proposed model efficiently captures periodic laws of key influencing factors, significantly boosting prediction accuracy and generalization capacity, thus possessing prominent theoretical and practical engineering application values.

Machine-learning modeling of pore pressure variation in geological CO2 storage reservoir