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Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique
Accurate prediction of monthly runoff is critical for optimal water resource allocation. However, previous studies mainly focused on the direct...
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Nonlinear Segmental Runoff Ensemble Prediction Model Using BMA
In this study, a novel nonlinear segmental runoff ensemble forecast model based on the Bayesian model averaging (BMA) algorithm (NLTM-BMA m (P-III)) is...
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Improving Multivariate Runoff Prediction Through Multistage Novel Hybrid Models
The exploitation of hydropower provides cleaner, more sustainable, and cheaper energy than fossil fuels. Therefore, hydropower offers prospects to...
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Unleashing the power of AI: revolutionizing runoff prediction beyond NRCS-CN method
Predicting runoff is vital for effectively planning and managing water resources within a watershed or river basin. This research aims to compare the...
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A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition
Predicting runoff, one of the fundamental operations in hydrology, is crucial for directing the complete exploitation and use of local water...
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Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning
In a changing environment, the increasing inconsistency of runoff series complicates the development of runoff forecasting models. Mutation tests...
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Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation
Logical development and effective use of water resources depend heavily on the practicability of runoff forecast. A monthly runoff interval...
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Monthly runoff prediction using gated recurrent unit neural network based on variational modal decomposition and optimized by whale optimization algorithm
To further increase the forecast precision of non-stationary non-linear monthly runoff series and improve the effectiveness of pretreatment of...
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Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study
Accurate and reliable runoff prediction is essential for water resources management. In this paper, a hybrid model STL-VMD-SFO-ESN which combines...
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Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions
In order to more reasonably predict runoff under extreme precipitation and corresponding meteorological conditions, and explore the influences of...
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A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology
Deep learning models have a high application value in runoff forecasting, but their prediction mechanism is difficult to interpret and their...
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Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction
Due to the influence of human regulation and storage factors, the runoff series monitored at the hydro-power stations often show the characteristics...
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A precipitation-runoff swift simulation model dedicated to emergency response to flood prediction
The Precipitation-Runoff Swift Simulation (PRSS) model was proposed to obtain the results of precipitation-runoff simulation quickly and effectively....
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A Runoff Prediction Model Based on Nonhomogeneous Markov Chain
Runoff prediction is one of the important research fields of hydrology. As for the runoff series with unstable, poor periodicity and non-obvious...
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Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks
Runoff forecasting is one of the important non-engineering measures for flood prevention and disaster reduction. The accurate and reliable runoff...
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Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks
Deep neural networks has been widely used in runoff forecasting and has achieved better performance than of conceptual hydrological models. However,...
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Effectiveness of Integrating Ensemble-Based Feature Selection and Novel Gradient Boosted Trees in Runoff Prediction: A Case Study in Vu Gia Thu Bon River Basin, Vietnam
Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially...
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A Hybrid Model Integrating Elman Neural Network with Variational Mode Decomposition and Box–Cox Transformation for Monthly Runoff Time Series Prediction
Precise and reliable monthly runoff prediction plays a vital role in the optimal management of water resources, but the nonstationarity and skewness...
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A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series
Accurately predicting runoff is crucial for managing water resources, preventing and mitigating floods, scheduling hydropower plant operations, and...
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A Nonlinear Dynamical Model for Monthly Runoff Forecasting in Situations of Small Samples
Runoff prediction with a small number of observations is an important but challenging topic in hydrological research. In this study, a coupled...
