Multilayer Perceptron Neural Network (MLP-NN) have been successfully applied to solve nonlinear problems in meteorology and oceanography. In this work, MLP-NN is applied to completely emulate an Extended Kalman Filter (EKF) in a data assimilation scenario. Data assimilation is a process for producing a good combination of data from observations and data from a mathematical model. This is a fundamental issue in an operational prediction system. The one-dimensional shallow water equation DYNAMO-1D is employed here for testing the assimilation schemes. The DYNAMO model is derived from depth-integrating the Navier-Stokes equations, in the case where the horizontal length scale is much greater than the vertical length scale, where the Coriolis force is also considered in atmospheric flows. Techniques, such as Extend Kalman Filter, are available to track non-linear dynamical models under certain conditions. Under strong non-linearity, the fourth-order moment EKF works well when applied to high-dimensional state space for data assimilation, but the computational burden is a barrier in this kind of application. Artificial Neural Network (ANN) is an alternative solution for this computational complexity problem, once the ANN is trained offline with a high order Kalman filter, even though this Kalman filter has high computational cost (which is not a problem during ANN training phase). The results achieved in this research encourage us to apply this technique on operational models. However, it is not yet possible to assure convergence in high dimensional problems.