Effects of uncertain knowledge in water level prediction using an LSTM Neural Network

Abstract: This article endeavours to demonstrate how uncertainty in the knowledge base and input data of artificial neural networks affects the accuracy of their predictions. In this paper, we introduce a new approach dealing with the omnipresent prediction error of machine learning methods. Our approach consists of specifically identifying and decreasing uncertainty in various scenarios in the knowledge base and database to increase the accuracy of the model forecasts. The data manipulation experiments in this paper prove that uncertainty in the model forecasts can be measured by observing the change in the prediction error. The use case is a water level prediction model for a closed harbour basin based on a Long short-term memory neural network. Our model, developed using standardised AI modules, predicts future water levels based on historical data and thus optimises energy efficiency and logistical processes for a tide-independent industrial port. Various scenarios for the origin of uncertainties in the datasets are simulated through the targeted manipulation of the historical dataset. We were able to show the significant impact of uncertainty on accuracy, which supports the idea of dealing with uncertainty to enhance artificial neural networks in logistic processes.

Keywords: uncertain knowledge, tide-independent harbour, machine learning, ai-modules, port-level forecast

DOI: 10.54941/ahfe1005922

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