[HTML][HTML] A review of the application of machine learning in water quality evaluation
M Zhu, J Wang, X Yang, Y Zhang, L Zhang… - Eco-Environment & …, 2022 - Elsevier
With the rapid increase in the volume of data on the aquatic environment, machine learning
has become an important tool for data analysis, classification, and prediction. Unlike …
has become an important tool for data analysis, classification, and prediction. Unlike …
Application of machine learning in groundwater quality modeling-A comprehensive review
Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The
prediction of groundwater pollution due to various chemical components is vital for planning …
prediction of groundwater pollution due to various chemical components is vital for planning …
Prediction of soil heavy metal immobilization by biochar using machine learning
Biochar application is a promising strategy for the remediation of contaminated soil, while
ensuring sustainable waste management. Biochar remediation of heavy metal (HM) …
ensuring sustainable waste management. Biochar remediation of heavy metal (HM) …
[HTML][HTML] Flood susceptibility modelling using advanced ensemble machine learning models
Floods are one of nature's most destructive disasters because of the immense damage to
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …
Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest
Groundwater quality in plains and basins of arid and semi-arid regions with increased
agriculture and urbanization development faces severe nitrate pollution, which is affected by …
agriculture and urbanization development faces severe nitrate pollution, which is affected by …
A review of earth artificial intelligence
Z Sun, L Sandoval, R Crystal-Ornelas… - Computers & …, 2022 - Elsevier
In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …
Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
M Panahi, N Sadhasivam, HR Pourghasemi… - Journal of …, 2020 - Elsevier
Freshwater shortages have become much more common globally in recent years. Water
resources that are naturally available beneath the surface are capable of reversing this …
resources that are naturally available beneath the surface are capable of reversing this …
Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques
Flood risk mapping and modeling is important to prevent urban flood damage. In this study,
a flood risk map was produced with limited hydrological and hydraulic data using two state …
a flood risk map was produced with limited hydrological and hydraulic data using two state …