Enhanced Preprocessing Feature Selection and Classification for Automatic Contamination Detection to Improve Water Quality

Abstract

The quality of drinking water has always been a powerful environmental determinant of health concern worldwide A secure and safe supply of drinking water is fundamental to public health Water contamination defined as the pollution of water bodies is an important factor that reduces the quality of drinking water This main aim of this research work is to design and develop algorithms based on data mining to detect the presence and absence of water contaminants The proposed water contamination detection system consists of three steps namely preprocessing feature selection and classification In preprocessing an enhanced KNearest Neighbour Imputation Method is used to handle the missing values in the water dataset The enhanced algorithm uses a pruning algorithm to reduce the size of the dataset by removing irrelevant instances KMeans algorithm to group similar instance together a weighted KNearest Neighbour Search and Imputation algorithm to impute the missing values a merging algorithm to combine all the imputed clusters to form a dataset with no missing values The feature selection is performed using a 2step algorithm which combines the advantages of filter and wrapper based feature selection algorithm This algorithm first uses a multiple filter algorithm to prune irrelevant features For this purpose the algorithm makes use of four filter based algorithms namely Mutual Information MI Pearson Correlation PC ChiSquared test CS and Fisher Criterion Score FS along with Markov Blanket Filter MBF The results are combined using a simple Boolean union operation This result is then used by the wrapper based algorithm which is designed as a method combining genetic algorithm and Support Vector Machine SVM Classifier The final result is a set of optimal features which have great positive impact on water contamination detection