Prediction of Discharges from Polders to ‘Boezem’ Canals with a Random Forest and an LSTM Model

Abstract

In this research the possibilities of the application of machine learning models at ‘Hoogheemraadschap van Delfland’ are studied. A random forest (RF) and an LSTM model are used for the prediction of the sum of the discharge in the next 2, 8 and 12 hours from the polders to the boezem canals. This research has showed the potential of machine learning models for the prediction of discharge for the considered pumping stations in the case area. This case area is clustered in the Sobek RR model as node 49. The RF and LSTM model are compared to the current Sobek RR model, the machine learning model of Delfland (ReRengAI) and a naïve model by calculating the root mean squared error (RMSE) for the last year of the dataset. For the prediction of the 2 hourly sum of Node 49 the RF model performs the best. Additionally, the performance of the RF model for the 12 hourly sum is satisfactory with a RMSE of 11,071 m3, though using a deep learning model (LSTM) the performance improved to a value of 10,181 m3 for the RMSE. Machine Learning models are known as black-box models and are hard to explain and interpret, which makes the practical implementation of these new models, despite good model results, challenging. Technical recommendations for implementation ML models are improving the quality and availability of the data, increasing the interpretability and explainability of the model, combining multiple objectives in the new model or combining a ML model with a physical model. Organizational recommendations are improving the knowledge about these models within the organization, studying the advantages of these models in comparison to the current model and involving different departments of the water authority in the development of these new models.