Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM).

Performance evaluation and verification of groundwater balance using WA + as a new water accounting system.

In this study, to increase the accuracy of determining the parameters of groundwater balance and finally the aquifer reservoir deficit, WA + as a new water accounting system has been applied by calculating six sheets in the Plasjan basin, central Iran. According to the results, the volume of rainfall and transfer flow to the basin was 548.8 MCM in the water year 2016-2017, which entered the hydrological cycle as input.

Novel approach for predicting groundwater storage loss using machine learning.

Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major groundwater aquifers are in a critical state.

Development of Multi-Criteria Decision Making Methods for Reduction of Seawater Intrusion in Coastal Aquifers Using SEAWAT Code.

Unplanned pumping of groundwater in the past two decades has caused many regional problems and tensions, leading to seawater intrusion into coastal aquifers. The main objective of this paper is the use of a Multi-Criteria Decision-Making (MCDM) approach combining with numerical simulation for reducing seawater intrusion in the Tajan coastal aquifer located on the southern seashores of the Caspian Sea, Iran, taking into account economic, social, and environmental issues. The MODFLOW code was used to simulate the groundwater flow.

The prediction of aquifer groundwater level based on spatial clustering approach using machine learning.

Water resources management requires a proper understanding of the status of available and exploitable water. One of the useful management tools is the use of simulation models that are highly efficient in spite of the complex problems in the groundwater sector. In the present study, three data-based models, namely, group method of data handling (GMDH), Bayesian network (BN), and artificial neural network (ANN), have been investigated to simulate the groundwater levels and assess the quantitative status of aquifers.

Metal concentrations in the groundwater in Birjand flood plain, Iran.

The objective of the present study was to investigate the concentration of metals (cadmium, lead, chromium, zinc, copper, and iron) were measured in groundwater at 30 sites from the Birjand flood plain of eastern Iran during the November 2010; identify any relationships between metals and pH, total hardness. Metal concentrations in the groundwater samples were decreased in sequence of Zn > Fe > Cu > Cr > Pb > Cd, respectively. The results showed that the overall mean concentrations of Cd, Pb, and Cr were at 0.