The adaptive neuro fuzzy inference system (ANFIS) has been proposed to model the time series of water quality data in this study. The biochemical oxygen demand data collected at the upstream catchment of Feitsui Reservoir in Taiwan for more than 20 years are selected as the target water quality variable. The classical statistical technique of the Box-Jenkins method is applied for the selection of appropriate input variables and data pre-processing of using differencing is implemented during the model development. The time series data obtained by ANFIS models are compared to those obtained by autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs). The results show that the ANFIS model identified at each sampling station is superior to the respective ARIMA and ANN models. The R values at all sampling stations of the training and testing datasets are 0.83–0.98 and 0.81–0.89, respectively, except at Huang-ju-pi-liao station. ANFIS models can provide accurate predictions for complex hydrological processes, and can be extended to other areas to improve the understanding of river pollution trends. The procedure of input selection and the pre-processing of input data proposed in this study can stimulate the usage of ANFIS in other related studies.
Time series modeling of biochemical oxygen demand at the upstream catchment of Feitsui Reservoir, Taiwan
Yung-Chia Chiu, Chih-Wei Chiang, Tsung-Yu Lee; Time series modeling of biochemical oxygen demand at the upstream catchment of Feitsui Reservoir, Taiwan. Hydrology Research 1 October 2016; 47 (5): 1069–1085. doi: https://doi.org/10.2166/nh.2016.136
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