A study of applying genetic programming to reservoir trophic state evaluation using remote sensor data

The aim of this paper is to demonstrate a genetic programming (GP) method and to apply it to monitor reservoir water quality using remote sensing images. Based on genetic algorithms, the relationships between input and output can be expressed as parse trees. A fittest function type can be obtained automatically from this method. The advantage of GP is that system identification can be provided in a transparent and structured way. GP is used to construct the relationship between chlorophyll concentration and spectral parameters of SPOT sensor data. The results show that the model has better performance than the traditional regression method.