Assessing optimization techniques for improving water quality model

In order to keep the "good" status of coastal water quality, it is essential monitor and assess frequently. The Water quality index (WQI) model one most widely used techniques for assessment quality. It consists five components, with indicator selection technique being more crucial components. Several studies conducted recently have shown that use existing results in a significant amount uncertainty produced final due inappropriate selection. present study carried out comprehensive various features (FS) selecting indicators develop an efficient WQI model. This aims analyse effects eighteen different FS techniques, including (i) nine filter methods, (ii) two wrapper (iii) seven embedded methods comparison performance WQI. total, fifteen combinations (subsets) were constructed, values calculated each combination using improvement methodology model's was tested machine-learning algorithms, which validated metrics. indicated tree-based random forest algorithm could be effective terms assessing water. Deep neural network showed better predicting accurately incorporating subset forest.