Neural network based light attenuation model for monitoring seagrass health

Light availability to seagrasses is a major criterion limiting the distribution of seagrasses. Decreased water clarity and resulting reduced light penetration have been cited as major factors responsible for the decline in seagrasses. Light attenuation coefficient is an important parameter that indicates the light attenuated by the water column and can thereby be an indicator of seagrass health. Though, in practice, linear light attenuation models have been commonly used, there is a need for a more accurate model that can take into account the non-linearities present in coastal and estuarine environments. This paper presents neural network-based light attenuation models for monitoring the seagrass health in the Indian River Lagoon, FL. For performance evaluation, results of the developed neural network models are compared with linear regression models, model trees, and support vector machines.