Indicating if water is safe for human consumption using an enhanced machine learning approach

Predicting water quality accurately is critically important in real-life water resource management. This work proposes an approach based on supervised machine learning to predict water quality. Motivated, by the success of the non-smooth loss function for supervised learning problems [22], we reformulate the learning problem as a regularized optimization problem whose fidelity term is the hinge loss function and the hypothesis space is a polynomial approximation. To deal with the non-differentiability of the loss function, a special smoothing function is proposed. Then, the obtained optimization problem is solved by an improved conjugate gradient algorithm. Finally,some experiments results are presented.