AI Medical Compendium Journal:
Journal of contaminant hydrology

Showing 11 to 17 of 17 articles

A novel two-step approach for optimal groundwater remediation by coupling extreme learning machine with evolutionary hunting strategy based metaheuristics.

Journal of contaminant hydrology
We propose a simulation-optimization (SO) model based on a novel two-step strategy for the optimal design of groundwater remediation systems. The SO models are developed by coupling simulation models directly or through the extreme learning machine (...

A stochastic modeling approach for analyzing water resources systems.

Journal of contaminant hydrology
Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated th...

Physics-informed deep learning for prediction of CO storage site response.

Journal of contaminant hydrology
Accurate prediction of the CO plume migration and pressure is imperative for safe operation and economic management of carbon storage projects. Numerical reservoir simulations of CO flow could be used for this purpose allowing the operators and stake...

Using a deep convolutional network to predict the longitudinal dispersion coefficient.

Journal of contaminant hydrology
Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of ...

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning.

Journal of contaminant hydrology
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological i...

Contaminant source identification using semi-supervised machine learning.

Journal of contaminant hydrology
Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sou...

Multi-machine learning methods for rapid and synergistic inversion of groundwater contamination source, hydrogeologic parameter and boundary condition.

Journal of contaminant hydrology
The application of machine learning methods to the groundwater pollution inversion problem has become a hot research topic in recent years. However, applying machine learning methods to achieve synergistic and rapid identification of pollution source...