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] Applications of machine learning to water resources management: A review of present status and future opportunities
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …
economic development of humans worldwide. Water is used for various purposes, including …
Monthly climate prediction using deep convolutional neural network and long short-term memory
Q Guo, Z He, Z Wang - Scientific Reports, 2024 - nature.com
Climate change affects plant growth, food production, ecosystems, sustainable socio-
economic development, and human health. The different artificial intelligence models are …
economic development, and human health. The different artificial intelligence models are …
Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain
This paper presents an analysis of trunk movement in women with postnatal low back pain
using machine learning techniques. The study aims to identify the most important features …
using machine learning techniques. The study aims to identify the most important features …
Advanced streamflow forecasting for Central European Rivers: the cutting-edge Kolmogorov-Arnold networks compared to Transformers
Accurate streamflow forecasting is crucial for effective water resource management, flood
mitigation, and maintaining ecological balance, especially in Central Europe's major rivers …
mitigation, and maintaining ecological balance, especially in Central Europe's major rivers …
[HTML][HTML] A critical review of RNN and LSTM variants in hydrological time series predictions
The rapid advancement in Artificial Intelligence (AI) and big data has developed significance
in the water sector, particularly in hydrological time-series predictions. Recurrent Neural …
in the water sector, particularly in hydrological time-series predictions. Recurrent Neural …
[HTML][HTML] Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions
A Mohammed, G Corzo - 2024 - Elsevier
The authors have plagiarized part of a paper that had already appeared in Hydrology and
Earth System Sciences, volume 26 (2022), 795–825. One of the conditions of submission of …
Earth System Sciences, volume 26 (2022), 795–825. One of the conditions of submission of …
[HTML][HTML] A novel urban heat vulnerability analysis: Integrating machine learning and remote sensing for enhanced insights
Rapid urbanization and climate change exacerbate the urban heat island effect, increasing
the vulnerability of urban residents to extreme heat. Although many studies have assessed …
the vulnerability of urban residents to extreme heat. Although many studies have assessed …
Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing
Climate change and urbanization have increased the frequency of floods worldwide,
resulting in substantial casualties and property loss. Accurate flood forecasting can offer …
resulting in substantial casualties and property loss. Accurate flood forecasting can offer …
A temporal fusion transformer deep learning model for long-term streamflow forecasting: a case study in the funil reservoir, Southeast Brazil
G Fayer, L Lima, F Miranda… - Knowledge …, 2023 - … journals.publicknowledgeproject.org
Water reservoirs play a critical role in water resource management systems, serving various
purposes such as water supply, hydropower generation, and flood control. Accurate long …
purposes such as water supply, hydropower generation, and flood control. Accurate long …