A review on the applications of machine learning for runoff modeling
B Mohammadi - Sustainable Water Resources Management, 2021 - Springer
The growing menace of global warming and restrictions on access to water in each region is
a huge threat to global hydrological sustainability. Hence, the perspective at which …
a huge threat to global hydrological sustainability. Hence, the perspective at which …
[HTML][HTML] Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
WM Ridwan, M Sapitang, A Aziz, KF Kushiar… - Ain Shams Engineering …, 2021 - Elsevier
Rainfall plays a main role in managing the water level in the reservoir. The unpredictable
amount of rainfall due to the climate change can cause either overflow or dry in the reservoir …
amount of rainfall due to the climate change can cause either overflow or dry in the reservoir …
Fractionation of dyes/salts using loose nanofiltration membranes: Insight from machine learning prediction
Wastewater (WW) served as the crucial indicator for sustainable development, human
health, and the ecosystem. Nanofiltration (NF) membranes are efficient in contaminants, dye …
health, and the ecosystem. Nanofiltration (NF) membranes are efficient in contaminants, dye …
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction
Accurate and reliable prediction of streamflow is vital to the optimization of water resources
management, reservoir flood operations, catchment, and urban water management. In this …
management, reservoir flood operations, catchment, and urban water management. In this …
New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system
Abstract Recently, hydrogen (H 2) gas has gained prodigious attention as a sustainable
energy carrier to reduce acute dependence on fossil fuels due to its fascinating properties …
energy carrier to reduce acute dependence on fossil fuels due to its fascinating properties …
A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem
Hydrological models play a crucial role in water planning and decision making. Machine
Learning-based models showed several drawbacks for frequent high and a wide range of …
Learning-based models showed several drawbacks for frequent high and a wide range of …
Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and …
Due to the significant energy and economic losses brought on by the global oil spill, there
has been an increased interest in oil-water separation. This study presents strong non-linear …
has been an increased interest in oil-water separation. This study presents strong non-linear …
Recovery of brine resources through crown-passivated graphene, silicene, and boron nitride nanosheets based on machine-learning structural predictions
The rising global demand for brine resources necessitates the exploration of alternative
sources to complement existing natural sources. It is imperative to explore innovative …
sources to complement existing natural sources. It is imperative to explore innovative …
Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models
Accurate and timely monitoring of streamflow and its variation is crucial for water resources
management in watersheds. This study aimed at evaluating the performance of two process …
management in watersheds. This study aimed at evaluating the performance of two process …
Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm
Reliable and accurate brain tumor segmentation is a challenging task even with the
appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic …
appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic …