| Abstrakt: |
Water pollution is one of the serious threats to the society, as water is the primary need of every organism thriving on earth. It is necessary to control and detect water pollution by assessing the quality of water. However, the production of wastewater is always there and is inevitable. Hence, it is equally important to treat the wastewater in a better way, such that the environment is not affected. The pollution control board has formulated certain standards, which provides the range of values for each pollutant and the feasible discharge locations. Taking these standards as the input for training the system, this work extracts basic statistical features such as mean, standard deviation, entropy and variance for training the classification system. The ensemble classification is incorporated, which includes k-nearest neighbour (k-NN), support vector machine (SVM) and extreme learning machine (ELM). The performance of the proposed approach is evaluated in terms of accuracy, sensitivity and specificity. The results of the proposed approach are found to be satisfactory. |
