{'fraction_non_alphanumeric': 0.014436958614051972, 'fraction_numerical': 0.0038498556304138597, 'mean_word_length': 5.011560693641618, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Prediction of the Water Level During Storm Situations using Neural Networks

This thesis provides an overview of the research and development of a water level prediction system for Hoek van Holland during storm situations. The prediction in this project is done with neural networks. Example storm situations are a very important factor for the quality of the neural network, but the database used in this project did not contain many severe storm situations. In this thesis several techniques and implementations are discussed for covering the lack of severe storm situations. In this project two neural network architectures are used, radial basis networks and feed-forward networks. This last architecture is working well for predicting storms. This network can predict surge levels with a standard deviation of 14,18 centimeter, six hours in advance. This result is better than the results obtained by current prediction methods. Our approach is data-driven and we expect that we can improve our results if more data becomes available.