Systematic review of using machine learning in imputing missing values
Missing data are a universal data quality problem in many domains, leading to misleading
analysis and inaccurate decisions. Much research has been done to investigate the different …
analysis and inaccurate decisions. Much research has been done to investigate the different …
Low-cost internet-of-things water-quality monitoring system for rural areas
Water is a vital source for life and natural environments. This is the reason why water
sources should be constantly monitored in order to detect any pollutants that might …
sources should be constantly monitored in order to detect any pollutants that might …
Improving the sediment and nutrient first-flush prediction and ranking its influencing factors: An integrated machine-learning framework
Pollutant first flush (FF) is a critical phenomenon in urban regions driven by multiple factors.
Therefore, having a robust mathematical FF definition and modeling tool along with …
Therefore, having a robust mathematical FF definition and modeling tool along with …
Robust imputation method with context-aware voting ensemble model for management of water-quality data
Water-quality monitoring and management are crucial for ensuring the safety and
sustainability of water resources. However, missing data is a frequent problem in water …
sustainability of water resources. However, missing data is a frequent problem in water …
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 …
Water quality prediction of the yamuna river in India using hybrid neuro-fuzzy models
The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle
swarm optimization, genetic algorithm, harmony search, and teaching–learning-based …
swarm optimization, genetic algorithm, harmony search, and teaching–learning-based …
The responses of river discharge and sediment load to historical land-use/land-cover change in the Mekong River Basin
TT Sam, DN Khoi - Environmental Monitoring and Assessment, 2022 - Springer
The large river basins throughout the world have undergone land-use/land-cover (LULC)-
induced changes in river discharge and sediment load. Evaluating these changes is …
induced changes in river discharge and sediment load. Evaluating these changes is …
The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction
Water, a priceless gift from nature, acts as Earth's matrix, medium, and life-sustaining
substance. While the planet is predominantly covered by water, only 3% is available as …
substance. While the planet is predominantly covered by water, only 3% is available as …
Assessing influential rainfall–runoff variables to simulate daily streamflow using random forest
F Vilaseca, A Castro, C Chreties… - Hydrological Sciences …, 2023 - Taylor & Francis
This work aims to improve the feature selection for data-driven rainfall–runoff models by
assessing the significance of each input variable in the learning process and analysing it …
assessing the significance of each input variable in the learning process and analysing it …
Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning
This study presented a novel paradigm for forecasting 12-step-ahead monthly precipitation
at 126 California gauge stations. First, the satellite-based precipitation time series from …
at 126 California gauge stations. First, the satellite-based precipitation time series from …