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Groundwater

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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...

A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA).

Journal of environmental management
Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) s...

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models.

Environmental science and pollution research international
Forecasting the irrigation groundwater parameters helps plan irrigation water and crop, and it is commonly expensive because it needs various parameters, mainly in developing nations. Therefore, the present research's core objective is to create accu...

Investigating the impact of sewer overflow on the environment: A comprehensive literature review paper.

Journal of environmental management
Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Sewer Overflow (SO) has been considered as a detrimental threat to our environment and health, which r...

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

Environmental science and pollution research international
To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) meth...

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 (...

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.

The Science of the total environment
The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the lo...