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Groundwater

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Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning.

Environmental science and pollution research international
The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to ...

A Novel Groundwater Burial Depth Prediction Model Based on Two-Stage Modal Decomposition and Deep Learning.

International journal of environmental research and public health
The variability of groundwater burial depths is critical to regional water management. In order to reduce the impact of high-frequency eigenmodal functions (IMF) generated by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN...

Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence.

Environmental science and pollution research international
This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two...

Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia.

The Science of the total environment
The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW sali...

Groundwater Quality: The Application of Artificial Intelligence.

Journal of environmental and public health
Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and ...

Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods.

Environmental science and pollution research international
Excessive withdrawal of groundwater for agricultural irrigation can cause seawater intrusion into coastal aquifers. Such a case will in turn results in deterioration of irrigation water quality. Determination of irrigation water quality with traditio...

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

Environmental monitoring and assessment
The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Ground...

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.

Chemosphere
Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW...

A novel groundwater burial depth prediction model-based on the combined VMD-WSD-ELMAN model.

Environmental science and pollution research international
The improvement of groundwater burial depth prediction accuracy is an important guiding significance for the development and management of groundwater resources. Groundwater burial depth sequence has the characteristics of uncertainty and nonlinearit...

Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks.

International journal of environmental research and public health
Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time seri...