HRL
Delay
TP
TN
FP
FN
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
References
1. Amit B, Brill E (2018) New approach for estimation of detention time and prediction of quality
in water networks. Water Qual Res J 53(2):72–85
2. Mounce S, Machell J, Boxall J (2012) Water quality event detection and customer complaint
clustering analysis in distribution systems. Water Supply 12(5):580–587
3. Knorr EM, Ng RT (1997) A unified approach for mining outliers. In: Proceedings conference of
the Centre for Advanced Studies on Collaborative Research (CASCON), Toronto, Canada
4. Knorr EM, Ng RT (1998) Algorithms for mining distance-based outliers in large datasets. In:
Proceedings of the international conference on very large data bases (VLDB), New York, NY
5. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: Proceedings
of the European conference on principles of knowledge discovery and data mining, Helsinki,
Finland
6. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large
data sets. In: Proceedings of the ACM SIGMOD international conference on management of
data (SIGMOD), Dallas, TX
7. Bay SD, Schwabacher M (2003) Mining distance-based outliers in near linear time with
randomization and a simple pruning rule. In: Proceedings of the international conference on
knowledge discovery and data mining (KDD), Washington, DC
8. Breunig MM, Kriegel H-P, Ng RT, Sander J (1999) OPTICS-OF: identifying local outliers. In:
Proceedings of the European conference on principles of data mining and knowledge discovery
(PKDD), Prague, Czech Republic
9. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local
outliers. In: Proceedings of the ACM SIGMOD international conference on management of
data (SIGMOD), Dallas, TX
10. Jin W, Tung A, Han J (2001) Mining top-n local outliers in large databases. In: Proceedings of
the ACM SIGKDD international conference on knowledge discovery and data mining
(SIGKDD), San Francisco, CA
11. Jie T, Hang L, Yunbo C, Zhaohui T (2005) Email data cleaning. KDD’05, August 21-24, 2005,
Chicago, IL, USA. Copyright 2005 ACM 1-59593-135-X/05/0008
Using Radial Basis Function for Water Quality Events Detection
165
Delay
TP
TN
FP
FN
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
References
1. Amit B, Brill E (2018) New approach for estimation of detention time and prediction of quality
in water networks. Water Qual Res J 53(2):72–85
2. Mounce S, Machell J, Boxall J (2012) Water quality event detection and customer complaint
clustering analysis in distribution systems. Water Supply 12(5):580–587
3. Knorr EM, Ng RT (1997) A unified approach for mining outliers. In: Proceedings conference of
the Centre for Advanced Studies on Collaborative Research (CASCON), Toronto, Canada
4. Knorr EM, Ng RT (1998) Algorithms for mining distance-based outliers in large datasets. In:
Proceedings of the international conference on very large data bases (VLDB), New York, NY
5. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: Proceedings
of the European conference on principles of knowledge discovery and data mining, Helsinki,
Finland
6. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large
data sets. In: Proceedings of the ACM SIGMOD international conference on management of
data (SIGMOD), Dallas, TX
7. Bay SD, Schwabacher M (2003) Mining distance-based outliers in near linear time with
randomization and a simple pruning rule. In: Proceedings of the international conference on
knowledge discovery and data mining (KDD), Washington, DC
8. Breunig MM, Kriegel H-P, Ng RT, Sander J (1999) OPTICS-OF: identifying local outliers. In:
Proceedings of the European conference on principles of data mining and knowledge discovery
(PKDD), Prague, Czech Republic
9. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local
outliers. In: Proceedings of the ACM SIGMOD international conference on management of
data (SIGMOD), Dallas, TX
10. Jin W, Tung A, Han J (2001) Mining top-n local outliers in large databases. In: Proceedings of
the ACM SIGKDD international conference on knowledge discovery and data mining
(SIGKDD), San Francisco, CA
11. Jie T, Hang L, Yunbo C, Zhaohui T (2005) Email data cleaning. KDD’05, August 21-24, 2005,
Chicago, IL, USA. Copyright 2005 ACM 1-59593-135-X/05/0008
Using Radial Basis Function for Water Quality Events Detection
165