Table 4 Comparison of the outcomes of the ISS algorithm and the other algorithms.

From: Examination of hydrological variations and their effect on water shortage trends and water-energy production using convolutional neural network and ISSA

Function

Index

EISS

WSO

SDO

GO

BOA

F1

Best

0.72

1.91

88.24

2.67

3.28

Mean

7.24

12.20

230.03

10.67

16.31

StD

4.21

6.61

233.17

10.54

13.85

F2

Best

3.83

5.62

6.02

8.62

6.20

Mean

41.39

41.28

72.47

96.94

74.08

StD

19.65

20.78

57.80

43.89

35.51

F3

Best

0.61

1.65

34.67

2.84

1.06

Mean

2.32

3.79

19.96

6.22

9.24

StD

0.01

2.70

9.28

0.01

7.88

F4

Best

2.74

4.97

5.23

11.98

6.06

Mean

6.18

18.62

14.67

14.16

6.52

StD

1.11

1.85

1.52

6.64

1.57

F5

Best

0.00

0.00

3.32

0.21

0.16

Mean

0.01

0.01

8.48

1.69

2.36

StD

0.00

0.00

0.73

1.32

2.22

F6

Best

0.00

0.00

0.11

1.02

2.54

Mean

0.00

0.00

1.95

1.26

1.59

StD

1.02

1.36

1.03

1.62

1.33

F7

Best

0.34

0.52

0.72

0.60

0.38

Mean

0.41

1.61

1.37

0.98

1.80

StD

0.11

0.12

0.19

0.19

0.14

F8

Best

5.31

8.06

9.72

8.19

24.41

Mean

6.59

11.02

12.78

19.20

24.91

StD

2.00

3.71

7.52

4.74

3.80

F9

Best

0.00

0.00

17.55

0.20

0.13

Mean

0.00

0.00

31.69

2.51

3.48

StD

0.00

0.00

9.55

1.85

2.88

F10

Best

0.22

0.32

4.15

4.41

8.13

Mean

4.03

7.52

15.40

21.30

8.03

StD

1.47

2.76

6.55

5.40

4.35

F11

Best

0.00

0.00

0.05

0.23

0.24

Mean

0.01

0.01

0.13

0.48

0.60

StD

0.00

0.00

0.07

0.18

0.10

F12

Best

0.00

0.00

0.00

0.00

0.00

Mean

0.00

0.00

0.00

0.00

0.00

StD

0.00

0.00

0.00

0.00

0.00