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Hyperparameter Tuning in a Machine Learning Prediction Model for Surface Water Quality Using High-Frequency Input Data

Author(s): Elisa Coraggio; Theo Tryfonas; Claire Gronow; Dawei Han

Linked Author(s): Dawei Han

Keywords: No Keywords

Abstract: A good understanding of water quality is fundamental for managing water resources in future scenarios that include water scarcity and climate change. Despite the advancement in technologies and high-frequency datasets for water quality in surface water becoming widely available, there is still insufficient knowledge on how to build effective prediction models using such datasets. This study proposes a technique for finding the suitable input features (including data frequency) needed to build a robust water quality prediction model that is able to predict the surface water body’s behaviour in the future.


Year: 2022

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