Water Quality Prediction Based on Wavelet Neural Networks and Remote Sensing

Wavelet artificial neural networks and remote sensing techniques can be used to estimate water quality variables such as Chlorophyll-a, turbidity and suspended solids. This paper describes empirical algorithms for the estimation of these variables incorporating information from the Operational Land Imager Sensor on board the Landsat-8 satellite. Neural networks are seasonally trained using data from the Cefni reservoir (Anglesey, U.K.), covering a variety of physical trophic status. Chlorophyll-a levels and the suspended solids are estimated from the reflectance in band-2 and band-4, while the turbidity values from reflectance in band-4. Experimental results show the potential of Landsat-8 as a substitute of Landsat-7 in water bodies quality monitoring. Moreover, predicted values obtained by using wavelet artificial neural networks fit well measured data and hence, such models provide accurate results therefore improving the efficiency in monitoring water quality parameters and contributing to possible decision making processes in the environmental management.