The paper presents an entropy-based method for designing an optimum bay water salinity monitoring network in San Francisco bay (S.F bay) considering maximum monitoring information and minimum data lost criteria. Due to cost concerns, it is necessary to design the optimal salinity monitoring network with a minimal number of sampling stations to provide reliable data. The monthly data recorded during January 1995 to December 2014 was obtained over 37 active stations located at S.F bay and is applied in the research. Transinformation entropy in discrete mode is used to calculate stations optimum distance. The discrete approach uses the frequency table to calculate transinformation measures. After calculating these measures, transinformation-distance (T-D) curve was developed. Then, the optimum distance between salinity monitoring stations was elicited from the curve. The study showed that the S.F bay salinity monitoring stations provided redundant information and existing stations can be reduced to 21 with an approximate distance of 7.5 km. The coverage of proposed monitoring network by using optimum distance is complete and the system doesn't generate redundant data. The results of this research indicate that transinformation entropy is a promising method for monitoring networks designing in bays such as those found in San Francisco bay.
- optimum distance
- San Francisco Bay
- transinformation entropy
- water quality monitoring networks
- First received 16 November 2015.
- Accepted in revised form 13 June 2016.
- © IWA Publishing 2016