Machine learning and Deep learning
Due to its high computing power, machine learning has shown a particular interest in processing and understanding of large and multifunctional data. In the case of environmental data, these are often complex and highly non-linear.
A deep learning based multiple regression network that consist an input layer, a multi-hidden layer with more the one hidden layer and an output layer. The nodes are fully connected. The number of input layer nodes is equal to the number of features of the input data. The more hidden layers, the higher the number of features needed to reduce the influence of under fitting or over fitting. Each hidden layer node is composed of neurons. The neurons contain both rectifier activation and aggregation function, when constructing the deep learning multiple regression model, the activation function in the default neuron is the Rectified Linear activation function, making the deep learning network neurons have sparse characteristics, which reduces the influence of overfitting while increasing the depth of the network, improving the training speed of the model, and effectively overcoming the problem of gradient disappearance. This function that we must define is responsible for creating the neural network model to be evaluated.
We use an appropriate deep learning based multiple regression algorithm using Keras library to improve nonlinear prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component, 10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2 metre temperature.
Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa.