Applied the gray theory for groundwater level supplement in Jiao-Shi hot spring area, Taiwan

The administration of hot spring in Taiwan has collected and supervised the data of hot spring level. However, the data is usually collected incompletely due to the special temperature and characteristics of hot spring, which influenced the function of instruments. For the purpose of the data analysis of hot spring level, the gray theory was conducted in this study to supplement the data of hot spring level. The complete part data of hot spring level were tested and practiced to predict the optimal model with gray prediction, and then the missing data were accomplished by the optimal model, which were arranged to build the complete and continues data. In this study, the data of hot spring level in Jiao-Shi in 2010 was inspected, which supplemented the data during 2009~2010. The results showed that the predicted deviation would increase if the batches of data were predictions. It was caused by the error of posterior predictions would be contained by the anterior, and the trend of the deviation would increase or decrease progressively in the system of gray prediction. Thus it was needed to operate with the divergence distance method to decrease the error. The results of test exhibited that there's the best model with 12 batches of data and 7 days of divergence distance in Hot Spring Park station. This study used the best models in different administrations of hot spring in the study to supplement the data of Hot Spring Park station during 2009~2010 respectively. The fragmented data of hot spring level in Jiao-Shi will be supplemented by this study.