AIMC Topic: Groundwater

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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...

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution.

Environmental pollution (Barking, Essex : 1987)
Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based o...

Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations.

The Science of the total environment
The monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like ...

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change.

Nature communications
In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21 century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites ...

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model.

Environmental science and pollution research international
The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two...