A New ANN-Markov chain methodology for water quality prediction

In recent years, water quality prediction has attracted many attentions of governments and researchers. The safety of water quality seriously affects the human health, fishery economy and agricultural activities. If an early prediction to the water quality with an acceptable accuracy can be achieved, the negative impacts will be minimized or even be avoided. Many researchers have applied artificial neural networks (ANNs) to build the water quality models for there is a complicated non-linear relation between the prediction variables and measured inputs. However the ANN models are easy to be over-fitting for training them needs a large of samples. As the objective of this study, artificial neural network and Markov chain approach are used to develop a new hybrid methodology for predicting the biochemical oxygen demand which is the main indicator of water quality. ANN produces the primary values and then the results are modified by three regression methods using the Markov transitional probability matrices respectively. We use a 27-year water quality data set of Tolo Harbor which only has a total of 439 samples to test our method. The results are validated and a better prediction accuracy of the new ANN-Markov Chain Methodology is demonstrated through three criteria.