Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence predict levels using these indicators, particularly regions experiencing drought subsidence. Precipitation, temperature, evapotranspiration precipitable water vapor (PWV) are important parameters levels. Two subsidence areas with different weather conditions were selected conduct a comprehensive on the effect of temperature precipitation level changes. The correct locations two determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images Sentinel-1A small baseline subset algorithm. interferograms processed tropospheric effects advanced integration method. Specifying exact areas, downscaled Statistical DownScaling Model (SDSM), synoptic observations, data, General Circulation (GCM). An Artificial Neural Network (ANN) was then employed as function including Global Positioning System (GPS)-based PWV index. trained ANN, along used over time periods. In first period, prediction performed current years performance method available whereas second for coming years, up until 2030. results confirmed high algorithm, importance predictions. Pearson correlation coefficient check relationship between variables. statistical significance coefficients tested at α=0.05. more than 80% cases, statistically significant, reaching 0.70 some months. It is also observed that an increase depth has obvious decrease rainfall.