Large-scale quantification of groundwater recharge threshold conditions using machine learning classifications: An attempt over the Australian continent

Abstract

The large-scale quantification of groundwater recharge threshold conditions is a bottleneck in the field of research, considering that groundwater recharge processes can be quite complex and affected by different factors in different regions. Traditional statistical approaches have either focused on local to regional scales or are limited to a single factor such as rainfall, resulting in a lack of a comprehensive understanding of groundwater recharge mechanisms on large scales. Considering that finding a threshold to instantly determine whether the groundwater is in the state of recharge or discharge is a classification task involving multiple factors, machine learning techniques with self-learning capabilities, efficient processing of big data, and mining relationships between multiple factors is, thus, applied to this study. In this study, three traditional machine learning classification techniques are explored; Classification and Regression Tree (CART), Random Forest (RF), and Logistical Regression (LR), to quantify specific threshold values for each condition (including rainfall, evaporation, soil moisture, runoff and vegetation) since they are more interpretable compared to advanced machine learning techniques such as neural networks. Using the Australian continent as an example, CART is the fastest method and provides an average classification accuracy (76%) at almost the same levels compared to RF (77–78%) and LR (79–80%). Meanwhile, CART is the only method to rank the importance of conditions while providing a specific threshold for each condition. When inferring the groundwater recharge mechanism through threshold conditions, the CART method suggests that a primary (the most important) threshold condition is sufficient for most parts of the Australian continent, e.g., rainfall as the primary threshold condition in northern Australia indicates a direct recharge mechanism. Only the coastal areas of southeastern and southwestern Australia are dominated by multiple threshold conditions, showing the complex groundwater recharge mechanism. Overall, the significance of this work is to provide the spatial maps of threshold conditions that mainly control groundwater recharge and their specific threshold values, which are crucial for managers to carry out a good plan over a large area. More importantly, this study demonstrates the potential of traditional machine learning methods to reveal groundwater recharge mechanisms, which may help further refine the development of groundwater models in the future.