Research Article

An Optimized Approach for Predicting Water Quality Features Based on Machine Learning

Table 6

Description of the attributes used for classification.

Attribute (7)
Instance (833)
Possible value
MinMaxMeanStd dev
I DOE-WQIDO010.5050.156
BOD010.2720.180
COD010.2540.162
SS010.1960.203
PH010.4910.127
NH3-NL010.1870.209

Attribute (13)
Instance (619)
Possible value
MinMaxMeanStd dev
National Water Quality StandardDO010.5220.152
BOD010.2610.175
COD010.2530.163
SS010.1850.192
PH010.4950.124
NH3-NL010.1790.210
COND010.2590.177
SAL010.2250.176
DS010.2140.191
TEMP010.4840.155
TUR010.2090.188
E-coli010.1160.174
Coliform010.1570.196

Attribute (28)
Instance (533)
Possible value
MinMaxMeanStd dev
All chemical contentsDO010.5380.149
BOD010.2440.171
COD010.2360.161
SS010.1700.181
PH010.4950.122
NH3-NL010.1560.193
COND010.2810.178
SAL010.2400.180
DS010.2040.177
TS010.1730.130
TEMP010.4720.157
TUR010.2050.180
E. coli010.1150.174
Coliform010.1580.197
NO3010.2100.201
Cl010.2170.172
PO4010.1860.198
As010.1680.138
Hg010.4120.414
Cd0100
Cr0100
Pb010.5920.341
Zn010.1910.182
Ca010.2640.223
Fe010.2710.204
K010.3140.158
Mg010.2160.169
Na010.1720.141