Optimal Placement of Water Quality Monitoring Stations in Sewer Systems: An Information Theory Approach☆

Abstract A core problem associated with the water quality monitoring in the sewer system is the optimal placement of a limited number of monitoring sites. A methodology is provided for optimally design water quality monitoring stations in sewer networks. The methodology is based on information theory, formulated as a multi-objective optimization problem and solved using NSGA-II. Computer code is written to estimate two entropy quantities, namely Joint Entropy, a measure of information content, and Total Correlation, a measure of redundancy, which are maximized and minimized, respectively. The test on a real sewer network suggests the effectiveness of the proposed methodology.

[1]  Rajesh Gupta,et al.  Sensor Placement Methods for Contamination Detection in Water Distribution Networks: A Review , 2014 .

[2]  R. A. Deininger,et al.  Optimal Locations of Monitoring Stations in Water Distribution System , 1992 .

[3]  Francesco Napolitano,et al.  Artificial Neural Networks and Entropy-based Methods to Determine Pressure Distribution in Water Distribution Systems , 2014 .

[4]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[5]  W. J. McGill.  Multivariate information transmission , 1954, Trans. IRE Prof. Group Inf. Theory.

[6]  Marshall E. Moss,et al.  An intercomparison of hydrological network-design technologies , 1991 .

[7]  Zal K. Davar,et al.  Hydrometric Network Evaluation: Audit Approach , 1990 .

[8]  Avi Ostfeld,et al.  Multiobjective Contaminant Sensor Network Design for Water Distribution Systems , 2008 .

[9]  Avi Ostfeld,et al.  Detecting Accidental Contaminations in Municipal Water Networks , 1998 .

[10]  W. B. Samuels,et al.  Wastewater Critical Infrastructure Security and Protection , 2011 .

[11]  Alexander Kraskov,et al.  Published under the scientific responsability of the EUROPEAN PHYSICAL SOCIETY Incorporating , 2002 .

[12]  Avi Ostfeld,et al.  Optimal Layout of Early Warning Detection Stations for Water Distribution Systems Security , 2004 .

[13]  Arun Kumar,et al.  DETECTING ACCIDENTAL CONTAMINATIONS IN MUNICIPAL WATER NETWORKS. AUTHORS' REPLY , 1999 .

[14]  Alexander Kraskov,et al.  Hierarchical Clustering Based on Mutual Information , 2003, ArXiv.

[15]  V. Singh,et al.  THE USE OF ENTROPY IN HYDROLOGY AND WATER RESOURCES , 1997 .

[16]  Zoran Kapelan,et al.  SLOTS: Effective Algorithm for Sensor Placement in Water Distribution Systems , 2010 .

[17]  Cristiana Di Cristo,et al.  Illicit intrusion characterization in sewer systems , 2017 .

[18]  Michael Satosi Watanabe,et al.  Information Theoretical Analysis of Multivariate Correlation , 1960, IBM J. Res. Dev..

[19]  Leonardo Alfonso,et al.  Information theory applied to evaluate the discharge monitoring network of the Magdalena River , 2013 .

[20]  A. Lobbrecht,et al.  Information theory–based approach for location of monitoring water level gauges in polders , 2010 .

[21]  A. Lobbrecht,et al.  Optimization of water level monitoring network in polder systems using information theory , 2010 .

[22]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[23]  J. Amorocho,et al.  Entropy in the assessment of uncertainty in hydrologic systems and models , 1973 .

[24]  Ilker T. Telci,et al.  Optimal water quality monitoring network design for river systems. , 2009, Journal of environmental management.

[25]  Olivier Piller,et al.  Battle of the Water Sensor Networks , 2008 .

[26]  Mustafa M. Aral,et al.  Optimal Design of Sensor Placement in Water Distribution Networks , 2010 .

[27]  Leonardo Alfonso,et al.  Multiobjective Optimization of Operational Responses for Contaminant Flushing in Water Distribution Networks , 2010 .

[28]  Marios M. Polycarpou,et al.  ITERATIVE DEEPENING OF PARETO SOLUTIONS IN WATER SENSOR NETWORKS , 2008 .

[29]  Paulin Coulibaly,et al.  Developments in hydrometric network design: A review , 2009 .

[30]  C. Di Cristo,et al.  SWMM5 Toolkit Development for Pollution Source Identification in Sewer Systems , 2014 .