Groundwater level response identification by hybrid wavelet-machine learning conjunction models using meteorological data.

Due to its heterogeneous and complex nature, groundwater modeling needs great effort to quantify the aquifer, a crucial tool for policymakers and hydrogeologists to understand the variations in groundwater levels (GWL). This study proposed a set of supervised machine learning (ML) models to delineate the GWL changes in the Zarand-Saveh complex aquifer in Iran using 15-year (2005-2020) monthly dataset. The wavelet transform (WT) procedure was also used to improve the GWL prediction ability of ML models for 3-month horizons using input datasets of precipitation, evapotranspiration, temperature, and GWL.

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.