The Implementation of Artificial Neural Network (ANN) in the Prediction of Tides Level Data in Indonesia

Abstract

Indonesia is currently focusing on its big goal to become The World’s Maritime Axis. For this reason, several sectors such as the infrastructure of the port, the development of the fishing, and tourism industry should be improved. The use of accurate tides level data is indispensable to support these developments. However, the number of instruments to observe tides data is limited compared to the covered area since Indonesia has the third longest coastline in the world. Recently, the frequent use of Artificial Intelligence (AI) has also offered an alternative solution to provide prediction data, including tides level data. Thereby, Artificial Neural Networks (ANN) as the subfield of AI is then chosen to make a prediction of tides level data. The type of ANN used in this study is two-layer Feed Forward Neural Network (FFNN). The previous observed tides data using atmospheric data (temperature and pressure) and moon position as the features are used to train the network. In order to evaluate the performance of ANN model, the result of the prediction is then compared to the observed tides level data using Automatic Weather Station (AWS). The result shows that the predicted tide level data has a strong correlation with the observed data with coefficient correlation of 0.9238. Furthermore, Root Mean Square Error (RMSE) as the statistics parameters to evaluate the performance of ANN model is found to be low around 0.077 meters. This preliminary result suggests that the FFNN has a good performance in predicting tides level data and therefore can be applied to provide tides level data on a larger scale in Indonesia.