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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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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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Assessing Extreme Monthly Runoff Over an Arid Basin Through Reanalysis Datasets
Water availability in arid basins is a serious concern and its quantification remains uncertain. Climate variability as the El Niño Southern...
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Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series
The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. Data preprocessing...
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A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model
Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well 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...
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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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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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An enhanced monthly runoff forecasting using least squares support vector machine based on Harris hawks optimization and secondary decomposition
Accurate and reliable monthly runoff predictions are crucial for dispatching, allocation, and planning management of water resources. This research...
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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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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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Monthly, Seasonal, and Annual Variations of Precipitation and Runoff Over West and Central Africa Using Remote Sensing and Climate Reanalysis
Precipitation and runoff variability over West and Central Africa is the major challenge of water resource management, which drives a range of...
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Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks
Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term...
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A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM
Precise predicting of rainfall is paramount for effective water resource management, ecological conservation, and the prevention of droughts and...
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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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Application of novel binary optimized machine learning models for monthly streamflow prediction
Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation...
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Development of a linear–nonlinear hybrid special model to predict monthly runoff in a catchment area and evaluate its performance with novel machine learning methods
Accurate forecasting of runoff as an important hydrological variable is a key task for water resources planning and management. Given the importance...
