Design of the groundwater level monitoring network using principal component analysis (PCA) technique.

Using an appropriate monitoring network is considered as an efficient option to manage the groundwater resources and reduce drilling of costly sampling wells. Principal component analysis (PCA) is one of the data reduction techniques used to extract essential components. The used techniques are based on the identification of those describing the variance of the system. In this paper, the PCA technique has been employed in order to identify the effective wells and remove the less important ones. For this purpose, 160 wells were constructed in the Salman Farsi Agro-Industry, located in Khuzestan province of Iran. The data are measured twice a month for 12 months. In this technique, variation factors called principal components are identified through considering the data structures. Using the PCA, the relative importance of each well has been calculated for the groundwater depth estimation. In the present study, the acceptable threshold has been taken to be 0.8 and therefore the number of wells in determining groundwater depth was reduced to 33 ones. Identifying the essential wells, the important points for sampling are identified and groundwater depth monitoring is performed only in these wells. This will save time and cost of groundwater level monitoring within the study area.