Neural network-based estimation of light attenuation coefficient

Seagrasses are marine plants that provide many services such as primary productivity, food web interactions, shelter, nutrient cycling and habitat stabilization that are essential to marine and estuarine ecosystems. Therefore, monitoring seagrass health is crucial for the existence of many marine aquatic plants and animals. The minimal light requirement of seagrasses is about 20-30% of the total light measured just below the surface. This is relatively high compared to terrestrial plants and phytoplankton, which underlines the importance of water transparency for these species. Hence, light penetration into estuarine waters is critical for seagrass survival. In this paper, an approach to estimate the light attenuation coefficient from water quality parameters using neural networks is proposed. The model is compared with linear regression models such as step-wise linear regression and linear least squares regression. The light attenuation model presented here can be used for monitoring water quality and thereby seagrass health.