Wildfire and power grid nexus in a changing climate

Nature Reviews Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

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Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4192 - 4192

Published: April 10, 2025

Coal is the main energy source in China, but coal mining a high-risk industry, making prevention and control of hazards an important topic. Constrained by complexity unpredictability underground spaces, current research on disaster technologies mainly focuses characteristics overlying strata laws mine pressure, resulting significant deficiencies accuracy. Given this, data-driven pressure prediction method proposed, which uses deep learning models to learn patterns existing data generate required predictions. This approach avoids challenges accurately extracting rock mass physical mechanical parameters geological structure modeling, thereby improving accuracy control. The stage working face exertion period prone disasters during mining. To achieve accurate task divided into three steps: first step predict support resistance ahead face, second classify labels coordinate units, third characteristic exertion. Deep were designed trained separately for each For step, Spatiotemporal sequence model was selected, achieved mean absolute error 4.65 kN prediction. image segmentation-based classification chosen, with reaching 97.77%. fusion consisting LSTM (Long Short-Term Memory) networks designed. 0.17 dynamic coefficient, maximum 810.93 period, 9.96 cycles duration, 92.35% type. Simulating actual situation application scenarios, input steps set as output from previous evaluated. 1035.21 82.90% units. In simulated scenario, there 9922 instances exertion, predicted 10,336 instances, 9046 them matching instances. evaluated 4946 included complete cycles. coefficient 0.21, 1218.31 kN, duration cycle 11.03 cycles, type 91.75%.

Language: Английский

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Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102755 - 102755

Published: Aug. 3, 2024

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models machine learning algorithms are the mainstream tools streamflow simulation. However, their inherent limitations, such as time-consuming large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve accuracy computational efficiency of simulation, which integrates Block-wise use TOPMODEL (BTOP) model into eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated using Latin hypercube sampling algorithm instead calibration reduce costs. Then, XGBoost combines with multi-source errors. which, serval input variable selection employed choose relevant inputs remove redundant information model. The validated compared standalone at three stations in Jialing River basin, China. results show that performance BTOP_XGB significantly better than models. NSE Beibei, Xiaoheba, Luoduxi increases by 54%, 21%, 83%, respectively. Meanwhile, time saved >90% original calibrated BTOP. less affected parameter sample sizes amounts, demonstrating robustness simplifies complexity enhances stability learning, jointly improving reliability provides potential shortcut over basins areas or limited observed data.

Language: Английский

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Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: 69(11), P. 1501 - 1522

Published: July 1, 2024

Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian Model Averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day ahead Dunhuang Oasis, northwest China. The efficiency of BMA compared with four decomposition-based machine deep models. Satisfactory were achieved all models at lead times; however, based on NSE values 0.976, 0.967, 0.957, the greatest accuracy for forecasts, respectively. Uncertainty analysis confirmed reliability yielding consistently accurate forecasts. Thus, could provide an efficient alternative approach multistep-ahead forecasting. incorporation data decomposition techniques (e.g. Variational mode decomposition) algorithms Deep belief network) into BMA, may serve as worthy technical references supervised systems scare

Language: Английский

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Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(9), P. 3657 - 3675

Published: July 17, 2024

Language: Английский

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Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102914 - 102914

Published: Nov. 1, 2024

Language: Английский

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Acoustic Feature Extraction Based on Wavelet Transform for Industrial Induction Motor-Driven Belt Conveyor Condition Monitoring

Oriental Journal of Physical Sciences, Journal Year: 2024, Volume and Issue: 9(1), P. 44 - 52

Published: Aug. 30, 2024

The industrial induction motor-driven belt conveyor is an essential component in manufacturing facilities. Any unexpected shutdown can lead to significant disruptions, resulting financial losses amounting thousands of dollars per hour. Unfortunately, efficient mechanisms for monitoring the conveyor's condition are often lacking. Therefore, it crucial ensure early, precise, and effective detection malfunctions conveyors. This necessitates identification distinctive anomalies stemming from initial damage rotating machinery motor components. paper presents a non-invasive acoustic technique designed specifically method employed relies on wavelet transform-based feature extraction, offering notable advantages terms classification accuracy, time efficiency, quantity vectors required classifier training.

Language: Английский

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Research on the Application of Maximum Entropy Based Algorithm in Precipitation Forecasting

Published: Aug. 9, 2024

Language: Английский

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