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 | Min | Max | Mean | Std dev | I DOE-WQI | DO | 0 | 1 | 0.505 | 0.156 | BOD | 0 | 1 | 0.272 | 0.180 | COD | 0 | 1 | 0.254 | 0.162 | SS | 0 | 1 | 0.196 | 0.203 | PH | 0 | 1 | 0.491 | 0.127 | NH3-NL | 0 | 1 | 0.187 | 0.209 |
| | Attribute (13) Instance (619) | Possible value | Min | Max | Mean | Std dev | National Water Quality Standard | DO | 0 | 1 | 0.522 | 0.152 | BOD | 0 | 1 | 0.261 | 0.175 | COD | 0 | 1 | 0.253 | 0.163 | SS | 0 | 1 | 0.185 | 0.192 | PH | 0 | 1 | 0.495 | 0.124 | NH3-NL | 0 | 1 | 0.179 | 0.210 | COND | 0 | 1 | 0.259 | 0.177 | SAL | 0 | 1 | 0.225 | 0.176 | DS | 0 | 1 | 0.214 | 0.191 | TEMP | 0 | 1 | 0.484 | 0.155 | TUR | 0 | 1 | 0.209 | 0.188 | E-coli | 0 | 1 | 0.116 | 0.174 | Coliform | 0 | 1 | 0.157 | 0.196 |
| | Attribute (28) Instance (533) | Possible value | Min | Max | Mean | Std dev | All chemical contents | DO | 0 | 1 | 0.538 | 0.149 | BOD | 0 | 1 | 0.244 | 0.171 | COD | 0 | 1 | 0.236 | 0.161 | SS | 0 | 1 | 0.170 | 0.181 | PH | 0 | 1 | 0.495 | 0.122 | NH3-NL | 0 | 1 | 0.156 | 0.193 | COND | 0 | 1 | 0.281 | 0.178 | SAL | 0 | 1 | 0.240 | 0.180 | DS | 0 | 1 | 0.204 | 0.177 | TS | 0 | 1 | 0.173 | 0.130 | TEMP | 0 | 1 | 0.472 | 0.157 | TUR | 0 | 1 | 0.205 | 0.180 | E. coli | 0 | 1 | 0.115 | 0.174 | Coliform | 0 | 1 | 0.158 | 0.197 | NO3 | 0 | 1 | 0.210 | 0.201 | Cl | 0 | 1 | 0.217 | 0.172 | PO4 | 0 | 1 | 0.186 | 0.198 | As | 0 | 1 | 0.168 | 0.138 | Hg | 0 | 1 | 0.412 | 0.414 | Cd | 0 | 1 | 0 | 0 | Cr | 0 | 1 | 0 | 0 | Pb | 0 | 1 | 0.592 | 0.341 | Zn | 0 | 1 | 0.191 | 0.182 | Ca | 0 | 1 | 0.264 | 0.223 | Fe | 0 | 1 | 0.271 | 0.204 | K | 0 | 1 | 0.314 | 0.158 | Mg | 0 | 1 | 0.216 | 0.169 | Na | 0 | 1 | 0.172 | 0.141 |
|
|