[M-GI25] Near Surface Investigation and Modeling for Groundwater Resources Assessment and Conservation

[MGI25-P03] Development of a Hydrogeological Apparent Model Using Geophysical Measurements and Machine Learning Methods

*Ping-Yu Chang^1,2,3 (1.Department of Earth Sciences, National Central University, Taiwan., 2.Earthquake Disaster & Risk Evaluation and Management Center, National Central University, Taiwan., 3.Center for Astronautical and Physics Engineering, National Central University, Taiwan.)

Keywords:Hydrogeological apparent mode, Geophysical exploration measurements, Machine learning methods

This research aims to develop a hydrogeological apparent model by integrating geophysical exploration measurements and analyzing them with machine learning methods. The apparent model is an advanced technology that can substitute traditional conceptual models and can provide a more accurate representation of the subsurface hydrogeological conditions. The study will involve collecting geophysical exploration measurements, such as electrical resistivity and transient electromagnetic survey data, from the target area. These measurements will be analyzed using machine learning algorithms to extract relevant information about the subsurface hydrogeological conditions. The information will be integrated to create an apparent model that can be used to predict subsurface water flow and storage. The results of this study will demonstrate the potential of the hydrogeological apparent model as a powerful tool for understanding subsurface hydrogeological conditions. By using machine learning methods, the apparent model can provide a more accurate and detailed representation of subsurface conditions than traditional conceptual models.