| Summary: | 碩士 === 國立成功大學 === 環境工程學系碩博士班 === 95 === Non-point source pollution is the major pollution in Taiwan reservoirs’ catchment. It includes a large mount of nutrients and suspended solids which cause serious effects of water resources management. Because of special climate in Taiwan, most of non-point source pollution load occur in few rainstorm events. Monitoring data of the rainstorms will generate significantly error because of hard sampling and the related data are extremely deficient.
This study adopts off-site sampling and continuous monitoring in Bajhang River to collect runoff and water quality data in the rainstorms, include turbidity and nitrate, to establish integrated rainfall, runoff and water quality database. Then apply Artificial Neural Network (ANN) to simulate runoff and water quality information to forecast changing in the future. The results of simulation can combine with ISCO Inc. 6200 auto-sampler to promote traditionally experiential sampling approach to forecasted sampling approach. Therefore, we can accurately evaluate the change of dissolvable and undissolvable pollutants phenomena in the rainstorm events.
The first results show that ANN is capable for simulating runoff and water quality. After we training and testing water level forecasted model, the average coefficient of determination (R2) of water level at three hour later is 0.918, the root-mean square error (RMSE) is 0.319m. The average R2 of turbidity at three hour later is 0.684, RMSE is 222NTU. The average R2 of nitrate at three hour later is 0.885, RMSE is 0.05ppm. The comparison results of simulated and observed suspended sediment loads at three hour later is only underestimating 1.34% in 2006/4/27 rainfall event and 2.42% in 5/2 rainfall event. It shows that ANN can provide very well performance for simulating suspended sediment loads at three hour later.
The second results show that the simulated suspended sediment loads of ANN forecasted sampling method is higher then traditional sampling method or Flow-stratified method, is close to real suspended sediment loads. Because ANN model can simulate sediment loads variation in the future, we can make a sampling arrangement in advance so that estimated pollution loads will be close to real loads.
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