Marine Dissolved Oxygen Prediction With Tree Tuned Deep Neural Network

Changes in the dissolved oxygen concentration of the ocean have important implications for marine ecosystems and global climate change. However, limited by measurement techniques, the hydrology data is not always complete. Thus, accurate prediction on marine dissolved oxygen concentration (MDOC), is a powerful supplement to the current observation data. Deep neural network is a powerful model to do the prediction, while it is usually difficult and time-consuming to tune its structure. Meanwhile, deep jointly informed neural network (DJINN) provides a user friendly method to tune the structure of neural networks. In this article, a deep learning-based model called marine deep jointly informed neural network (M-DJINN), is proposed to predict MDOC. M-DJINN improves DJINN performance via initializing the weights of neural network using a zero-mean Gaussian distribution with a variance related to the number of neurons in the neighbor layer. In M-DJINN, to get an ideal deep neural network structure, users only need to tune the tree number and max tree depth. While predicting MDOC on World Ocean Database 2013(WOD13) data with M-DJINN, the novel model proves better than DJINN on both accuracy and convergency. When the max tree depth is set to 10, the mean squared error (MSE) is reduced by 17.6% compared with DJINN. It can be concluded from the experiments that with enough training data, the performance of M-DJINN may keep improving by deepening the neural networks.