Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

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Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

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Flood Forecasting Method and Application Based on Informer Model

Water, 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

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Modeling the effect of meteorological drought on lake level changes with machine learning techniques

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 227 - 246

Published: Jan. 1, 2025

Language: Английский

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Novel methodology for prediction of missing values in River flow based on convolution neural networks: Principles and application in Iran country

Physics 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

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Hybrid emotional neural networks and novel multi-model stacking algorithms for multi-lake water level fluctuation modeling

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

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Rationally Turbulent Expectations Chapter 5: Prediction Markets

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Development of a Bayesian Network-Based Parallel Mechanism for Lower Limb Gait Rehabilitation

Biomimetics, 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

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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed

Journal 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

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