Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 11, 2025
Language: Английский
Citations
5Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228
Published: Feb. 13, 2024
Language: Английский
Citations
14Water, Journal Year: 2024, Volume and Issue: 16(5), P. 765 - 765
Published: March 4, 2024
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number data acquisition devices, explosive growth multidimensional increasingly demanding prediction accuracy, classical parameter models, traditional machine learning algorithms are unable meet high efficiency precision requirements tasks. In recent years, deep represented by convolutional neural networks, recurrent networks Informer models have achieved fruitful results in time series The model is used predict flood flow reservoir. At same time, compared with method LSTM model, how apply field improve accuracy studied. 28 Wan’an Reservoir control basin from May 2014 June 2020 were used, areal rainfall five subzones outflow two reservoirs as inputs processes different sequence lengths outputs. show that has good applicability forecasting. length 4, 5 6, higher better than other under length, will decline certain extent increase length. stably predicts peak better, its average difference maximum smallest. As increases, fields less 15% decreases. Therefore, can be one methods, it provides new scientific decision-making basis for reservoir control.
Language: Английский
Citations
9Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 227 - 246
Published: Jan. 1, 2025
Language: Английский
Citations
0Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2025, Volume and Issue: unknown, P. 103875 - 103875
Published: Jan. 1, 2025
Language: Английский
Citations
0Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)
Published: Feb. 1, 2025
Language: Английский
Citations
0Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: March 12, 2025
Language: Английский
Citations
0SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Language: Английский
Citations
0Biomimetics, Journal Year: 2025, Volume and Issue: 10(4), P. 230 - 230
Published: April 8, 2025
This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, loss coordination during rehabilitation. To achieve this, it proposes an innovative design method for a rehabilitation training system based on Bayesian networks parallel mechanisms. A network model is constructed expert knowledge structural mechanics analysis, considering key factors such as scenarios, motion trajectory deviations, goals. By utilizing characteristics mechanisms, we designed device that supports multidimensional correction. three-dimensional digital developed, multi-posture ergonomic simulations are conducted. The focuses quantitatively assessing kinematic hip, knee, ankle joints while wearing device, establishing comprehensive evaluation includes range (ROM), dynamic load, optimization matching trajectories. Kinematic analysis verifies reasonable, aiding in improving patients’ gait, enhancing strength, restoring flexibility. achieves personalized goal through probability updates. mechanisms significantly expands joint motion, hip sagittal plane mobility reducing thereby validating notable effect
Language: Английский
Citations
0Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(2)
Published: April 2, 2025
ABSTRACT Floods are major natural disasters that present considerable challenges to socioeconomic and ecological systems. Flash floods highly nonlinear exhibit rapid spatiotemporal variability. Existing methods struggle capture these features, leading suboptimal long‐term peak flood prediction accuracy. This study proposes a hierarchical model based on clustering enhance forecasting accuracy in the Heshengxi watershed. We employ STGCN GWN models with attention mechanism. Enhanced loss functions further refine Results show method is an effective means of extracting features by addressing variability parameters for different magnitudes. The integration Graph Convolutional Time Aware enables recognize characteristics, overcoming limitations prevailing ensuring forecast optimized function improves performance, resulting significant improvement prediction, reduction 0.26% relative error model. framework provides solution warning, emergency response, optimal scheduling. It also demonstrates potential deep learning field intelligent hydrological forecasting.
Language: Английский
Citations
0