Machine Learning Algorithms Investigation for Urban Drainage Decision Systems: Overview

Urban drainage systems may derive advantages from the implementation of machine learning methods for decision-making and cleansing operations. Conventional decision support systems are rendered ineffective in tackling the intricate and indeterminate aspects of urban planning concerns. This paper provides an overview of machine learning methods applied in modeling urban drainage systems, which are classified into five distinct approaches: Supervised learning, unsupervised learning, deep learning, Reinforcement learning, and finally hybrid approaches combining two or more previous algorithms. This study explores also diverse datasets that researchers can utilize in their scientific investigations. The present related works exploration reveals that a majority of studies lean towards reinforcement learning and deep learning methods, predominantly utilizing local datasets. Consequently, we have compiled a table containing some reference datasets. These advancements result in enhanced accuracy and efficiency in predicting and managing urban drainage systems.