MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

[1]  Optimal estuarine sediment monitoring network design with simulated annealing. , 2006, Journal of environmental management.

[2]  J. W. Groenigen,et al.  Constrained optimisation of soil sampling for minimisation of the kriging variance , 1999 .

[3]  Hiroshi Ishikawa,et al.  Exact Optimization for Markov Random Fields with Convex Priors , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Patricia L. Smith,et al.  Geostatistical Error Management: Quantifying Uncertainty for Environmental Sampling and Mapping , 2001, Technometrics.

[5]  INTEGRATING MULTICRITERIA ANALYSIS AND GIS FOR ASSESSING RAINGAGE WORTH WITHIN AN ESTABLISHED NETWORK 1 , 2004 .

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  G Passarella,et al.  Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection , 2008, Environmental monitoring and assessment.

[8]  R Bellman,et al.  On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Matthew Richey,et al.  The Evolution of Markov Chain Monte Carlo Methods , 2010, Am. Math. Mon..

[10]  R. Bilonick An Introduction to Applied Geostatistics , 1989 .

[11]  Don L. Stevens,et al.  Spatial properties of design-based versus model-based approaches to environmental sampling , 2006 .

[12]  M. Vurro,et al.  Cokriging Optimization of Monitoring Network Configuration Based on Fuzzy and Non-Fuzzy Variogram Evaluation , 2003, Environmental monitoring and assessment.

[13]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[14]  Josiane Zerubia,et al.  An adaptive simulated annealing cooling schedule for object detection in images , 2007 .

[15]  Alfred Stein,et al.  Constrained Optimization of Spatial Sampling using Continuous Simulated Annealing , 1998 .

[16]  Emanuele Barca,et al.  A methodology for rapid assessment of the environmental status of the shallow aquifer of “Tavoliere di Puglia” (Southern Italy) , 2011, Environmental monitoring and assessment.

[17]  Peter Rossmanith,et al.  Simulated Annealing , 2008, Taschenbuch der Algorithmen.

[18]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[19]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[20]  Maria da Conceição Cunha,et al.  Water Distribution Network Design Optimization: Simulated Annealing Approach , 1999 .

[21]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[22]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[23]  Sheldon Howard Jacobson,et al.  The Theory and Practice of Simulated Annealing , 2003, Handbook of Metaheuristics.

[24]  Deepak Dutta,et al.  Design and Optimization of a Ground Water Monitoring System Using GIS and Multicriteria Decision Analysis , 1998 .

[25]  G. Pieters,et al.  Optimizing spatial sampling for multivariate contamination in urban areas , 2000 .

[26]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[27]  John N. Tsitsiklis,et al.  Markov Chains with Rare Transitions and Simulated Annealing , 1989, Math. Oper. Res..

[28]  Antônio José da Silva Neto,et al.  Design and Identification Problems of Rotor Bearing Systems Using the Simulated Annealing Algorithm , 2012 .

[29]  Eulogio Pardo-Igúzquiza,et al.  Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing , 1998 .

[30]  Giuseppe Passarella,et al.  Assessment of the Optimal Sampling Arrangement Based on Cokriging Estimation Variance Reduction Approach , 1997 .

[31]  Jinfeng Wang,et al.  A spatial sampling optimization package using MSN theory , 2011, Environ. Model. Softw..

[32]  James P. Hughes,et al.  Data requirements for kriging: Estimation and network design , 1981 .

[33]  S. Rouhani Variance Reduction Analysis , 1985 .

[34]  H. Winkels,et al.  Optimal cost - effective sampling for monitoring and dredging of contaminated sediments , 1997 .

[35]  Clayton V. Deutsch,et al.  Practical considerations in the application of simulated annealing to stochastic simulation , 1994 .

[36]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..

[37]  Lara Fontanella,et al.  Optimal spatial sampling schemes for environmental surveys , 2004, Environmental and Ecological Statistics.

[38]  Yaghout Nourani,et al.  A comparison of simulated annealing cooling strategies , 1998 .

[39]  Don L. Stevens,et al.  VARIABLE DENSITY GRID‐BASED SAMPLING DESIGNS FOR CONTINUOUS SPATIAL POPULATIONS , 1997 .

[40]  Emanuele Barca,et al.  A software for optimal information baseddownsizing/upsizing of existing monitoring networks. , 2011 .

[41]  F. Ungaro,et al.  Predicting Shallow Water Table Depth at Regional Scale: Optimizing Monitoring Network in Space and Time , 2013, Water Resources Management.

[42]  C. Sparrow The Fractal Geometry of Nature , 1984 .

[43]  J. Delhomme Kriging in the hydrosciences , 1978 .

[44]  Noel A Cressie,et al.  Spatial prediction from networks , 1990 .

[45]  M. Vurro,et al.  A Probabilistic Methodology to Assess the Risk of Groundwater Quality Degradation , 2002, Environmental monitoring and assessment.

[46]  Maria C. Cunha,et al.  Comparison of Variance‐Reduction and Space‐Filling Approaches for the Design of Environmental Monitoring Networks , 2007, Comput. Aided Civ. Infrastructure Eng..

[47]  P. Diggle,et al.  Model‐based geostatistics , 2007 .

[48]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[49]  Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization , 1998 .

[50]  Emanuele Barca,et al.  A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy) , 2010, Environmental monitoring and assessment.

[51]  Evaggelia Pitoura,et al.  Comparing Diversity Heuristics , 2009 .

[52]  Patrick Siarry,et al.  A theoretical study on the behavior of simulated annealing leading to a new cooling schedule , 2005, Eur. J. Oper. Res..

[53]  G. Christakos,et al.  Sampling design for classifying contaminant level using annealing search algorithms , 1993 .

[54]  Gijs Rennen,et al.  Subset selection from large datasets for Kriging modeling , 2008 .

[55]  Maria da Conceição Cunha,et al.  Groundwater Monitoring Network Optimization with Redundancy Reduction , 2004 .