dc.description.abstract | In recent years, the demand for water greatly increases with Taiwan’s population growing cause the water quality and supply become an important issue. Although Taiwan has abundant rainfall, which is not easy to retain surface water, and cause the groundwater become an important water resource. The harmful chemicals coming from inappropriate land use permeate through unsaturated soil and ultimately reach the underlying aquifer system. Groundwater quality in shallow aquifer is thus significantly affected by the land use and unsaturated soil. This study’s target is to develop a predictive model for occurrence of nitrate pollution in Taiwan coupled with artificial neural network. First, the method of factor analysis is applied. According to the results of factor analysis, it is indicated that the nitrate, fruit trees, and gravel layer are positively correlated. Based on the result, the scores for factor 3 used as the input data of the artificial neural network, and the nitrate concentration is the output result. Then, based on previous studies, other parameters were added, including Cl^-, SO_4^(2-), TOC, Fe^(3+), Mn^(2+), Ca^(2+), Mg^(2+), Na^(2+), K^+, HCO_3^- and As. The result of artificial neural network show that the model with 2 hidden layers and 12 neurons in each hidden layers has the best predictive effect. The determination coefficient is 0.664. This framework as the best model for predicting the concentration of nitrate. The study uses the final model to predict, R^2 can reach 0.95, which achieves good prediction performance, and provides a good prediction model for predicting the groundwater nitrate vulnerability. The government can strength land use management or take a better plan to prevent or control groundwater pollution and plan a comprehensive water quality protection plan to maintain the safety of Taiwan residents using groundwater.
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