A knowledge-based analysis tool to assess the THMs pollutant total exposure index and cumulative risk

Chlorination has made water supply safe from bacteria and viruses, and has almost completely eliminated risks of waterborne diseases such as typhoid fever, cholera and dysentery. However, the health benefit of chlorination has introduced some possible risks from the by-products of the disinfection process. In this work, we propose the use of a learning algorithm 'support vector machine (SVM)' for the prediction of the water pollutant total exposure index (TEI) and cumulative risk (CR). The SVM is an artificial intelligent approach that could capture the input/output mapping from the given data. Support vector machines were developed based on the structural risk minimization principle from statistical learning theory in which the empirical risk and the machine complexity are optimized. The proposed algorithm was validated by using statistical measurements on experimental data different from those used for the training (out-of-sample testing) to evaluate the algorithm's 'generalizability'. The experimental results demonstrate a good correlation between the SVM predicted values and those conventionally estimated using probabilistic (Monte Carlo) simulations. In both cases, the TEI and CR values were estimated using a set of data extracted from the national database IRIS. The SVM, once trained with representative data, could be used easily and effectively in comparison with other conventional estimation methods of TEI and CR. © 2009 Earthscan.