Groundwater Quality Monitoring Network Optimization Using Cluster Analysis: A Case Study of the Gareh-Baygone Aquifer, Shib-Kuh Plain, Shur Watershed of Iran

Managers need affordable, scalable, and reliable groundwater monitoring systems to collect critical groundwater data. In addition to the lack of water quality monitoring in the study area, the existing monitoring networks are not designed according to standard guidelines, and there is no ongoing assessment of these networks. Whether the monitoring networks have a great or small number of sites can lead to significant issues. This chapter aims to evaluate the monitoring networks of the changes in groundwater quality of the Shib-Kuh aquifer established in 2005 in South West of Iran. Additionally, the study aims to improve the monitoring networks and save expenses. In this aquifer, the groundwater main chemical anions, cations, EC, TDS, TH, SAR and pH are measured in 20 sites. For spatial-temporal optimization of underground water monitoring networks, new approaches have been investigated using methods based on statistics and geostatistics. There are many methods to determine the number of clusters in cluster analysis, which has a wide range of applied sciences. Grouping data into meaningful clusters is a popular problem omnipresent in the recent scientific literature having practical importance in various applications, such as investigating earthquakes, climate changes, and in biology, agriculture etc. The statistical cluster analysis methods are used and observed, variables and sampling sites are analyzed and evaluated. The results showed the probability of about 25 percent reduction in the sites. The outcome also proved that it was possible for some of the measuring parameters to have been removed. The cluster analysis method is a suitable way to evaluate the quality of establishment as well as the function of the monitoring networks of water resources. Through the application of this method, the number of sites, variables, or both of these factors can be optimized and this optimization leads to upgrading of monitoring networks.