Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation
Runoff forecasting is an important approach for flood mitigation. Many machine learning
models have been proposed for runoff forecasting in recent years. To reconstruct the time …
models have been proposed for runoff forecasting in recent years. To reconstruct the time …
Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach
The input selection process for data-driven rainfall-runoff models is critical because input
vectors determine the structure of the model and, hence, can influence model results. Here …
vectors determine the structure of the model and, hence, can influence model results. Here …
[HTML][HTML] Evaluation of transformer model and self-attention mechanism in the Yangtze River basin runoff prediction
Abstract Study region In the Yangtze River basin of China. Study focus We applied a
recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used …
recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used …
A comparison between wavelet based static and dynamic neural network approaches for runoff prediction
In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of
neural network models are commonly used in hydrology. Furthermore, the wavelet coupled …
neural network models are commonly used in hydrology. Furthermore, the wavelet coupled …
Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling
This study investigates the effect of inputs used on event-based runoff forecasting by ANFIS.
Fifteen ANFIS models were compared, differentiated by the choice of rainfall and/or …
Fifteen ANFIS models were compared, differentiated by the choice of rainfall and/or …
Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques
Input selection for data-driven rainfall-runoff models is an important task as these models
find the relationship between rainfall and runoff by direct map** of inputs to output. In this …
find the relationship between rainfall and runoff by direct map** of inputs to output. In this …
Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach
This study presents a novel approach of using the hybrid Wavelet Gene Expression
Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data …
Programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data …
Study on the stability and disaster mechanism of layered soil slopes under heavy rain
Y Li, K Xue, Y Zhao, C Wang, J Bi, T Wang… - Bulletin of Engineering …, 2023 - Springer
Under the influence of rainfall, the failure mechanism and stability of layered slopes is one of
the current hot topics. This paper aims to examine the influence of rainstorm conditions on …
the current hot topics. This paper aims to examine the influence of rainstorm conditions on …
Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model
An ensemble artificial neural network (ENN) based hybrid function approximator (named
PEK), integrating the partial mutual information (PMI) based separate input variable …
PEK), integrating the partial mutual information (PMI) based separate input variable …
Improving event-based rainfall–runoff modeling using a combined artificial neural network–kinematic wave approach
LHC Chua, TSW Wong - Journal of Hydrology, 2010 - Elsevier
The results of a study using a combined artificial neural network–kinematic wave (ANN-KW)
approach to simulate event-based rainfall–runoff process are reported in this paper. Three …
approach to simulate event-based rainfall–runoff process are reported in this paper. Three …