Discrete entropy theory for optimal redesigning of salinity monitoring network in San Francisco bay
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 from January 1995 to December 2014 were obtained over 37 active stations located in S.F. bay and is applied in the research. Transinformation entropy in discrete mode is used to calculate the stations' optimum distance. The discrete approach uses the frequency table to calculate transinformation measures. After calculating these measures, a transinformation–distance (T-D) curve is developed. Then, the optimum distance between salinity monitoring stations is elicited from the curve. The study shows that the S.F. bay salinity monitoring stations provide redundant information and the existing stations can be reduced to 21 with an approximate distance of 7.5 km. The coverage of the proposed monitoring network by using the optimum distance is complete and the system does not generate redundant data. The results of this research indicate that transinformation entropy is a promising method for the design of monitoring networks in bays such as those found in San Francisco bay.