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Using Radial Basis Function for Water Quality Events Detection

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ICT for Smart Water Systems: Measurements and Data Science

Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 102))

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Abstract

The current chapter demonstrates utilization of radial basis function (RBF) as a tool for detection and classification of abnormal events in water quality. The methodology is based on calibration of a RBF based on historical true events classified by human experts. The aim of the process is selection of parameters that ensure zero false negative events. The chapter describes the main method of using RBF and then compares four different kernel functions which are used for implementing the RBF. The case study part of the chapter illustrates actual analysis of real-world data as well as an illustrative example. The chapter concludes with some practical advice on how kernel functions should be selected for this task.

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Notes

  1. 1.

    ISO24522 is under publication procedures and will be available during winter 2019.

  2. 2.

    NTU are the standard units for measuring turbidity.

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Author information

Authors and Affiliations

  1. Faculty of Technology Management (MOT), Holon Institute of Technology (HIT), Holon, Israel

    Eyal Brill

Authors
  1. Eyal Brill
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Corresponding author

Correspondence to Eyal Brill .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Institute of Information Science and Technologies (CNR-ISTI), Pisa, Italy

    Andrea Scozzari

  2. Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK

    Steve Mounce

  3. Department of Civil Engineering, University of Bristol, Bristol, UK

    Dawei Han

  4. National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Napoli, Italy

    Francesco Soldovieri

  5. Hydroinformatics Chair, IHE Institute for Water Education and Water Resources Section, Delft University of Technology, Delft, The Netherlands

    Dimitri Solomatine

Appendix: Result of Run 1

Appendix: Result of Run 1

HRL

Delay

TP

TN

FP

FN

2.58

10

4

0

21

0

2.59

10

4

0

21

0

2.6

10

4

1

20

0

2.61

10

4

2

19

0

2.62

10

3

3

18

1

2.63

10

3

6

15

1

2.64

10

3

9

12

1

2.65

10

3

9

12

1

2.66

10

3

9

12

1

2.67

10

1

10

11

3

2.68

10

1

10

11

3

2.69

10

1

11

10

3

2.7

10

0

21

0

4

2.71

10

0

21

0

4

2.72

10

0

21

0

4

2.58

15

4

1

20

0

2.59

15

4

1

20

0

2.6

15

4

2

19

0

2.61

15

4

2

19

0

2.62

15

3

3

18

1

2.63

15

3

6

15

1

2.64

15

3

9

12

1

2.65

15

3

10

11

1

2.66

15

2

10

11

2

2.67

15

1

10

11

3

2.68

15

1

10

11

3

2.69

15

1

12

9

3

2.7

15

0

21

0

4

2.71

15

0

21

0

4

2.72

15

0

21

0

4

2.58

30

4

3

18

0

2.59

30

4

3

18

0

2.6

30

4

4

17

0

2.61

30

4

5

16

0

2.62

30

3

5

16

1

2.63

30

3

7

14

1

2.64

30

2

10

11

2

2.65

30

2

10

11

2

2.66

30

2

10

11

2

2.67

30

1

10

11

3

2.68

30

1

10

11

3

2.69

30

1

16

5

3

2.7

30

0

21

0

4

2.71

30

0

21

0

4

2.72

30

0

21

0

4

2.58

60

4

5

16

0

2.59

60

4

5

16

0

2.6

60

4

6

15

0

2.61

60

3

7

14

1

2.62

60

3

8

13

1

2.63

60

3

9

12

1

2.64

60

1

10

11

3

2.65

60

1

10

11

3

2.66

60

1

10

11

3

2.67

60

0

10

11

4

2.68

60

0

10

11

4

2.69

60

0

17

4

4

2.7

60

0

21

0

4

2.71

60

0

21

0

4

2.72

60

0

21

0

4

2.58

90

4

7

14

0

2.59

90

4

7

14

0

2.6

90

4

8

13

0

2.61

90

2

10

11

2

2.62

90

2

10

11

2

2.63

90

2

12

9

2

2.64

90

1

12

9

3

2.65

90

1

12

9

3

2.66

90

1

12

9

3

2.67

90

0

12

9

4

2.68

90

0

12

9

4

2.69

90

0

18

3

4

2.7

90

0

21

0

4

2.71

90

0

21

0

4

2.72

90

0

21

0

4

2.58

120

4

8

13

0

2.59

120

4

8

13

0

2.6

120

4

9

12

0

2.61

120

2

11

10

2

2.62

120

2

11

10

2

2.63

120

2

13

8

2

2.64

120

1

13

8

3

2.65

120

1

13

8

3

2.66

120

1

13

8

3

2.67

120

0

13

8

4

2.68

120

0

13

8

4

2.69

120

0

18

3

4

2.7

120

0

21

0

4

2.71

120

0

21

0

4

2.72

120

0

21

0

4

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Brill, E. (2019). Using Radial Basis Function for Water Quality Events Detection. In: Scozzari, A., Mounce, S., Han, D., Soldovieri, F., Solomatine, D. (eds) ICT for Smart Water Systems: Measurements and Data Science. The Handbook of Environmental Chemistry, vol 102. Springer, Cham. https://doi.org/10.1007/698_2019_424

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