[HTML][HTML] A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research
The presence of various forms of heavy metals (HMs)(eg, Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg,
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
Suspended sediment load prediction using sparrow search algorithm-based support vector machine model
Prediction of suspended sediment load (SSL) in streams is significant in hydrological
modeling and water resources engineering. Development of a consistent and accurate …
modeling and water resources engineering. Development of a consistent and accurate …
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and
land development. Machine learning (ML)(eg, artificial neural networks) has been …
land development. Machine learning (ML)(eg, artificial neural networks) has been …
Water quality prediction using SWAT-ANN coupled approach
Efficient and accurate prediction of river water quality is challenging due to the complex
hydrological and environmental processes affecting their nature. The challenge is even …
hydrological and environmental processes affecting their nature. The challenge is even …
Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new …
Many recent studies propose wavelet-based hydrological and water resources forecasting
models that have been incorrectly developed and that cannot properly be used for real …
models that have been incorrectly developed and that cannot properly be used for real …
Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile
Suspended sediment has significant effects on reservoir storage capacity, the operation of
hydraulic structures and river morphology. Hence, modeling suspended sediment loads …
hydraulic structures and river morphology. Hence, modeling suspended sediment loads …
Review of watershed-scale water quality and nonpoint source pollution models
Watershed-scale nonpoint source (NPS) pollution models have become important tools to
understand, evaluate, and predict the negative impacts of NPS pollution on water quality …
understand, evaluate, and predict the negative impacts of NPS pollution on water quality …
Estimating water levels and discharges in tidal rivers and estuaries: Review of machine learning approaches
Understanding the dynamics of tidal rivers and estuaries is critical for reliable water
management. Recently, the use of Machine Learning (ML) has increased in favor of …
management. Recently, the use of Machine Learning (ML) has increased in favor of …
Monitoring on triboelectric nanogenerator and deep learning method
As the basic hydrological parameters, the concentration and type of suspended sediment
particles greatly influence the aquatic ecological environment and the water conservancy …
particles greatly influence the aquatic ecological environment and the water conservancy …