29. M. Di Zio, N. Fursova, T. Gelsema, S. Gießing, U. Guarnera, J. Petrauskienė, L. Quensel-von
Kalben, M. Scanu, K. O. Bosch, M. Loo and K. Walsdorfer, "Methodology for data validation
1.0," 2016
30. Venkatasubramanian V, Rengaswamy R, Kavuri S, Yin K (2003) A review of process fault
detection and diagnosis, part III: process history based methods. Comput Chem Eng
27:327–346
31. NITS (1996) Federal standard 1037C data stream. https://www.its.bldrdoc.gov/fs-1037/dir-010/
_1451.htm. Accessed 20 Oct 2018
32. B. Gaag and J. Volz, "Real-time on-line monitoring of contaminants in water. Developing a
research strategy from utility experiences and needs.," 2008
33. S. Sun, J. Bertrand-krajewski, A. Lynggaard-Jensen, J. Broeke, F. Edthofer, M. Céu Almeida,
M. Silva Ribeiro and J. Menaia, "Literature review of data validation methods," 2011
34. EPA (2006) Data quality assessment, a reviewers guide. EPA QA/G-9R, US-Environmental
Protection Agency, Washington
35. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri S (2003) A review of process fault
detection and diagnosis, part I: quantitative methods. Comput Chem Eng 27:293–311
36. Branisqvljevic N, Kapelan Z, Prodanovic D (2011) Improved real-time data anomaly detection
using context classification. Hydroinformatics
37. Waal T (2013) Selective editing: a quest for efficiency and data quality. J Off Stat 29:473–488
38. Wilson PW (1993) Detecting outliers in deterministic nonparametric frontier models with
multiple outputs. J Bus Econ Stat 11:319–323
39. von Asmuth J (2011) Over de kwaliteit, frequentie en validatie van druksensorreeksen. KWR
2010.001. KWR Watercycle Research Institute, Nieuwegein
40. McKenna H, Klise K, Cruz V, Wilson M (2007) Event detection from water quality time series.
In: Proceedings of world environmental and water resources congress, ASCE, Reston
41. Mounce S, Mounce R, Jackson T, Austin J, Boxall J (2014) Pattern matching and associative
artificial neural networks for water distribution system time series data analysis. J Hydroinf
16:617–632
42. van Thienen P, Pieterse-Quirijnse I, Kater H, Duifhuizen J (2012) Nieuwe
lekverliesbepalingsmethoden voor het drinkwaterdistributienet. H2O
43. Bakker M (2004) Optimised control and pipe burst detection by water demand
forecastingOptimised control and pipe burst detection by water demand forecasting
44. Thienen PV, Vertommen I (2015) Automated feature recognition in CFPD analyses of DMA or
supply area flow data. J Hydroinf 18(3):514–530
45. Clarke R (2013) Calculating uncertainty in regional estimates of trend in streamflow with both
serial and spatial correlations. Water Resour Res 49:7120–7125
46. Hirsch RM, Moyer DL, Archfield SA (2010) Weighted regressions on time, discharge, and
season (WRTDS), with an application to Chesapeake bay river inputs. J Am Water Resour
Assoc 46:857–880
47. Furnival GM (1971) All possible regressions with less computation. Technometrics
13:403–408
48. Hocking R, Leslie N (1967) Selection of the best subset in regression analysis. Technometrics
9:531
49. Schatzoff M, Fienberg S, Tsao R (1968) Efficient calculations of all possible regressions.
Technometrics 10:768
50. Lomb NR (1976) Least-squares frequency analysis of unequally spaced data. Astrophys Space
Sci 39:447–462
51. Castelletti A, Galelli S, Ratto M, Soncini-Sessa R, Young PC (2012) A general framework for
dynamic emulation modelling in environmental problems. Environ Model Soft 34:5–18
52. Galelli S, Humphrey G, Maier H, Castelletti A, Dandy G, Gibbs M (2014) An evaluation
framework for input variable selection algorithms for environmental data-driven models.
Environ Model Softw:33–51
108
M. Castro-Gama et al.
Kalben, M. Scanu, K. O. Bosch, M. Loo and K. Walsdorfer, "Methodology for data validation
1.0," 2016
30. Venkatasubramanian V, Rengaswamy R, Kavuri S, Yin K (2003) A review of process fault
detection and diagnosis, part III: process history based methods. Comput Chem Eng
27:327–346
31. NITS (1996) Federal standard 1037C data stream. https://www.its.bldrdoc.gov/fs-1037/dir-010/
_1451.htm. Accessed 20 Oct 2018
32. B. Gaag and J. Volz, "Real-time on-line monitoring of contaminants in water. Developing a
research strategy from utility experiences and needs.," 2008
33. S. Sun, J. Bertrand-krajewski, A. Lynggaard-Jensen, J. Broeke, F. Edthofer, M. Céu Almeida,
M. Silva Ribeiro and J. Menaia, "Literature review of data validation methods," 2011
34. EPA (2006) Data quality assessment, a reviewers guide. EPA QA/G-9R, US-Environmental
Protection Agency, Washington
35. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri S (2003) A review of process fault
detection and diagnosis, part I: quantitative methods. Comput Chem Eng 27:293–311
36. Branisqvljevic N, Kapelan Z, Prodanovic D (2011) Improved real-time data anomaly detection
using context classification. Hydroinformatics
37. Waal T (2013) Selective editing: a quest for efficiency and data quality. J Off Stat 29:473–488
38. Wilson PW (1993) Detecting outliers in deterministic nonparametric frontier models with
multiple outputs. J Bus Econ Stat 11:319–323
39. von Asmuth J (2011) Over de kwaliteit, frequentie en validatie van druksensorreeksen. KWR
2010.001. KWR Watercycle Research Institute, Nieuwegein
40. McKenna H, Klise K, Cruz V, Wilson M (2007) Event detection from water quality time series.
In: Proceedings of world environmental and water resources congress, ASCE, Reston
41. Mounce S, Mounce R, Jackson T, Austin J, Boxall J (2014) Pattern matching and associative
artificial neural networks for water distribution system time series data analysis. J Hydroinf
16:617–632
42. van Thienen P, Pieterse-Quirijnse I, Kater H, Duifhuizen J (2012) Nieuwe
lekverliesbepalingsmethoden voor het drinkwaterdistributienet. H2O
43. Bakker M (2004) Optimised control and pipe burst detection by water demand
forecastingOptimised control and pipe burst detection by water demand forecasting
44. Thienen PV, Vertommen I (2015) Automated feature recognition in CFPD analyses of DMA or
supply area flow data. J Hydroinf 18(3):514–530
45. Clarke R (2013) Calculating uncertainty in regional estimates of trend in streamflow with both
serial and spatial correlations. Water Resour Res 49:7120–7125
46. Hirsch RM, Moyer DL, Archfield SA (2010) Weighted regressions on time, discharge, and
season (WRTDS), with an application to Chesapeake bay river inputs. J Am Water Resour
Assoc 46:857–880
47. Furnival GM (1971) All possible regressions with less computation. Technometrics
13:403–408
48. Hocking R, Leslie N (1967) Selection of the best subset in regression analysis. Technometrics
9:531
49. Schatzoff M, Fienberg S, Tsao R (1968) Efficient calculations of all possible regressions.
Technometrics 10:768
50. Lomb NR (1976) Least-squares frequency analysis of unequally spaced data. Astrophys Space
Sci 39:447–462
51. Castelletti A, Galelli S, Ratto M, Soncini-Sessa R, Young PC (2012) A general framework for
dynamic emulation modelling in environmental problems. Environ Model Soft 34:5–18
52. Galelli S, Humphrey G, Maier H, Castelletti A, Dandy G, Gibbs M (2014) An evaluation
framework for input variable selection algorithms for environmental data-driven models.
Environ Model Softw:33–51
108
M. Castro-Gama et al.