Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan.
The back-propagation (BP) artificial neural network (ANN) is applied to forecast the variation of the quality of groundwater in the blackfoot disease area in Taiwan. Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters as input variables for seawater intrusion and three concentration parameters as input variables for arsenic pollutant, respectively, whereas models B and C used only one concentration parameter for each. Furthermore, model C used seasonal data from two seasons to train the ANN, whereas models A and C used only data from one season. The results indicate that model C outperforms models A and B. Model C can describe complex variation of groundwater quality and be used to perform reliable forecasting. Moreover, the number of hidden nodes does not significantly influence the performance of the ANN model in training or testing.
Kuo
Yi Ming
YM
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan, 10617, ROC.
Liu
Chen Wuing
CW
Lin
Kao Hung
KH
D020261:Arsenic Poisoning / Q000453:epidemiology*; D004196:Disease Outbreaks; D005544:Forecasting; D006801:Humans; D016571:Neural Networks, Computer; D010523:Peripheral Nervous System Diseases / Q000453:epidemiology; D015203:Reproducibility of Results; D012621:Seasons; D012623:Seawater; D012989:Soil Pollutants / Q000506:poisoning*; D013624:Taiwan; D014873:Water Pollutants / Q000506:poisoning*; D014881:Water Supply
D012989:Soil Pollutants; D014873:Water Pollutants