Evaluation of ground water monitoring network by principal component analysis.

Principal component analysis is a data reduction technique used to identify the important components or factors that explain most of the variance of a system. This technique was extended to evaluating a ground water monitoring network where the variables are monitoring wells. The objective was to identify monitoring wells that are important in predicting the dynamic variation in potentiometric head at a location. The technique is demonstrated through an application to the monitoring network of the Bangkok area. Principal component analysis was carried out for all the monitoring wells of the aquifer, and a ranking scheme based on the frequency of occurrence of a particular well as principal well was developed. The decision maker with budget constraints can now opt to monitor principal wells which can adequately capture the potentiometric head variation in the aquifer. This was evaluated by comparing the observed potentiometric head distribution using data from all available wells and wells selected using the ranking scheme as a guideline.