Area of research
Water Science and Technology · Environmental Engineering
Research interest
Research interests include Hydrology and Watershed Management Studies, Hydrological Forecasting Using AI, Phase Equilibria and Thermodynamics, and Flood Risk Assessment and Management.
RMC: advancing daily runoff forecasting with a unified cross-scale deep learning approach
A synergistic framework integrating CPO-VMD with BiLSTM-TimesNet for accurate prediction of nonlinear and nonstationary runoff time series.
Hybrid framework for robust runoff forecasting via decomposition and machine learning.
Enhancing Daily Runoff Forecasting with a Dual-Domain Deep Learning Model
DMEL: A novel dual-modal ensemble learning architecture for multi-step runoff prediction
Monte-Carlo-assisted endo-exo temporal transformer for high-confidence interval forecasting of daily runoff
A Hybrid Deep Learning Framework Integrating Multi-Strategy Optimization and Error Correction for Ensemble Monthly Runoff Prediction
Mamba-enhanced multi-scale state space model for robust runoff prediction under data-scarce conditions across climatic zones
The CCVCLSA model: A novel approach to medium- and long-term runoff prediction integrating multiple techniques
MamGA: a deep neural network architecture for dual-channel parallel monthly runoff prediction based on mamba and depth-gated attention layer
Enhancing monthly runoff prediction: a data-driven framework integrating variational mode decomposition, enhanced artificial rabbit optimization, support vector regression, and error correction
Multi-strategy enhanced artificial rabbit optimization algorithm for solving engineering optimization problems
Development and evaluation of a novel DTIB-LSSVM model for efficient and accurate runoff forecasting
Revolutionizing flood forecasting by integrating rainfall-runoff correlation analysis with advanced deep learning techniques
Season-Aware Ensemble Forecasting with Improved Arctic Puffin Optimization for Robust Daily Runoff Prediction Across Multiple Climate Zones
A deep learning runoff prediction model based on wavelet decomposition and dynamic feature fusion.
An efficient parallel runoff forecasting model for capturing global and local feature information.
Comprehensive performance assessment of state-of-the-art metaheuristic algorithms for multi-scenario reservoir flood control optimization
Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems
Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization
DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion
Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series
An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems
A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology
A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction
A stacking ensemble machine learning model for improving monthly runoff prediction
A hybrid annual runoff prediction model using echo state network and gated recurrent unit based on sand cat swarm optimization with Markov chain error correction method
A Multi-strategy Slime Mould Algorithm for Solving Global Optimization and Engineering Optimization Problems
MSBES: an improved bald eagle search algorithm with multi- strategy fusion for engineering design and water management problems
Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems