Abstract: A method for predicting water clarity at a plurality of water depths for a location including providing training data to a neural network, the training data representative of water measurements at the location, thereafter receiving inputs including temperature, salinity, tidal information, water depth, and sediment data, and generating values for optical attenuation at a wavelength at a plurality of depths. In one embodiment, a default cloudy day algorithm operates at all times and a clear sky algorithm operates only when clear satellite images are available.