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Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm
Urban flooding is a global metropolitan problem; therefore, establishing reliable streamflow
forecasting models is critical for flood control and planning in urban areas. Furthermore …
forecasting models is critical for flood control and planning in urban areas. Furthermore …
Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models
W Fang, S Huang, K Ren, Q Huang, G Huang… - Journal of …, 2019 - Elsevier
The applicability of the traditionally used overall decomposition-based (ODB) sampling
technique in the development of forecasting models is controversial. This study first conducts …
technique in the development of forecasting models is controversial. This study first conducts …
A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction
L Tao, X He, J Li, D Yang - Journal of Hydrology, 2021 - Elsevier
In this study, a multiscale long short-term memory model with attention mechanism (MLSTM-
AM) is proposed to improve the accuracy of monthly precipitation forecasting. In the MLSTM …
AM) is proposed to improve the accuracy of monthly precipitation forecasting. In the MLSTM …
A hybrid VMD-SVM model for practical streamflow prediction using an innovative input selection framework
E Meng, S Huang, Q Huang, W Fang, H Wang… - Water Resources …, 2021 - Springer
Some previous studies have proved that prediction models using traditional overall
decomposition sampling (ODS) strategy are unreasonable because the subseries obtained …
decomposition sampling (ODS) strategy are unreasonable because the subseries obtained …
Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks
Spring discharges from karst aquifers are results of spatially and temporally complex
hydrologic processes, such as precipitation, surface runoff, infiltration, groundwater flow as …
hydrologic processes, such as precipitation, surface runoff, infiltration, groundwater flow as …
A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors
Rainfall prediction is of vital importance in water resources management. Accurate long‐
term rainfall prediction remains an open and challenging problem. Machine learning …
term rainfall prediction remains an open and challenging problem. Machine learning …
Trends and spatio-temporal variability of summer mean and extreme precipitation across South Korea for 1973–2022
Climate change has altered the frequency, intensity, and timing of mean and extreme
precipitation. Extreme precipitation has caused tremendous socio-economic losses, and …
precipitation. Extreme precipitation has caused tremendous socio-economic losses, and …
[HTML][HTML] Identification of decadal trends and associated causes for organic and elemental carbon in PM2. 5 at Canadian urban sites
Chemically resolved data for fine particulate matter (PM 2.5) have been collected across
Canada since 2003 through the National Air Pollution Surveillance (NAPS) network. Seven …
Canada since 2003 through the National Air Pollution Surveillance (NAPS) network. Seven …
Future precipitation extremes in China under climate change and their physical quantification based on a regional climate model and CMIP5 model simulations
P Qin, Z **e, J Zou, S Liu, S Chen - Advances in Atmospheric Sciences, 2021 - Springer
The atmospheric water holding capacity will increase with temperature according to
Clausius-Clapeyron scaling and affects precipitation. The rates of change in future …
Clausius-Clapeyron scaling and affects precipitation. The rates of change in future …
Time-frequency analysis and simulation of the watershed suspended sediment concentration based on the Hilbert-Huang transform (HHT) and artificial neural network …
QJ Liu, HY Zhang, KT Gao, B Xu, JZ Wu, NF Fang - Catena, 2019 - Elsevier
Suspended sediment concentration (SSC) time series are highly nonlinear and
nonstationary due to numerous influencing factors that can be characterized by specific time …
nonstationary due to numerous influencing factors that can be characterized by specific time …