A review of hybrid deep learning applications for streamflow forecasting
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …
applications have garnered significant interest in the hydrological community. Despite the …
[HTML][HTML] A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments
Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers
on water resources, essential for water resources management. The present study aims to …
on water resources, essential for water resources management. The present study aims to …
Water quality prediction based on machine learning and comprehensive weighting methods
X Wang, Y Li, Q Qiao, A Tavares, Y Liang - Entropy, 2023 - mdpi.com
In the context of escalating global environmental concerns, the importance of preserving
water resources and upholding ecological equilibrium has become increasingly apparent …
water resources and upholding ecological equilibrium has become increasingly apparent …
Application of novel artificial bee colony optimized ANN and data preprocessing techniques for monthly streamflow estimation
Streamflow estimation is important in hydrology, especially in drought and flood-prone
areas. Accurate estimation of streamflow values is crucial for the sustainable management of …
areas. Accurate estimation of streamflow values is crucial for the sustainable management of …
Monthly rainfall forecasting modelling based on advanced machine learning methods: Tropical region as case study
Existing forecasting methods employed for rainfall forecasting encounter many limitations,
because the difficulty of the underlying mathematical proceeding in dealing with the …
because the difficulty of the underlying mathematical proceeding in dealing with the …
[HTML][HTML] Daily runoff forecasting using novel optimized machine learning methods
Accurate runoff forecasting is crucial for effective water resource management, yet existing
models often face challenges due to the complexity of hydrological systems. This study …
models often face challenges due to the complexity of hydrological systems. This study …
[PDF][PDF] Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine …
Water resource management is critical in the face of climate change to reduce water scarcity
and meet the demands of an expanding population. Prioritization of watersheds has gained …
and meet the demands of an expanding population. Prioritization of watersheds has gained …
Short–long-term streamflow forecasting using a coupled wavelet transform–artificial neural network (WT–ANN) model at the Gilgit River Basin, Pakistan
Streamflow forecasting is highly crucial in the domain of water resources. For this study, we
coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit …
coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit …
Review of recent trends in the hybridisation of preprocessing-based and parameter optimisation-based hybrid models to forecast univariate streamflow
Forecasting river flow is crucial for optimal planning, management, and sustainability using
freshwater resources. Many machine learning (ML) approaches have been enhanced to …
freshwater resources. Many machine learning (ML) approaches have been enhanced to …
A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models
The main goal of this study is to enhance the precision and reliability of monthly runoff
forecasts within the complex Navrood watershed, situated in northern Iran. The innovative …
forecasts within the complex Navrood watershed, situated in northern Iran. The innovative …