| Title |
Study on the Modelling of Algal Dynamics in Lake Paldang Using Artificial Neural Networks |
| Authors |
박혜경 ( Hae Kyung Park ) ; 김은경 ( Eun Kyoung Kim ) |
| Keywords |
Algal bloom; Artificial neural network; Lake Paldang; Sensitivity analysis; pH; Short-term-ahead models |
| Abstract |
Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted well compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH COD TP determined most the dynamics of chlorophyll a although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model`s performance. |