Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island

Abstract

Barbados is heavily reliant on groundwater resources for its potable water supply, with over 80% of the island’s water sourced from aquifers. The ability to meet demand will become even more challenging due to the continuing climate crisis. The consequences of climate change within the Caribbean region include sea level rise, as well as hydrometeorological effects such as increased rainfall intensity, and declines in average annual rainfall. Scientifically sound approaches are becoming increasingly important to understand projected changes in supply and demand while concurrently minimizing deleterious impacts on the island’s aquifers. Therefore, the objective of this paper is to develop a physics-based groundwater model and surrogate models using machine learning (ML), which provide decision support to assist with groundwater resources management in Barbados. Results from the study show that a single continuum conceptualization is adequate for representing the island’s hydrogeology as demonstrated by a root mean squared error and mean absolute error of 2.7 m and 2.08 m between the model and observed steady-state hydraulic head. In addition, we show that data-driven surrogates using deep neural networks, elastic networks, and generative adversarial networks are capable of approximating the physics-based model with a high degree of accuracy as shown by R-squared values of 0.96, 0.95, and 0.95, respectively. The framework and tools developed are a critical step towards a digital twin that provides stakeholders with a quantitative tool for optimal management of groundwater under a changing climate in Barbados. These outputs will provide sound evidence-based solutions to aid long-term economic and social development on the island.

Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale

“…Machine and statistical learning algorithms (e.g., those documented in [1][2][3]) are increasingly adopted for solving a variety of practical problems in hydrology ( [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]) and beyond ( [19][20][21][22][23]). Among the entire pool of such algorithms, the tree-based ensemble ones (i.e., those combining decision trees under properly designed ensemble learning strategies; [24]) are of special interest for many practical problems, as most of their software implementations offer high predictive performance with low computational cost, together with high automation and some degree of explainability ( [25,26]).…”

Metamodelling of Naturalised Groundwater Levels at a Regional Level in New Zealand

“…The metamodelling approach uses existing numerical groundwater models to develop significantly lower-cost estimates of groundwater level for continuous, and potentially for near real-time, decision-making [23][24][25]. These metamodels, such as Linear Regression and machine learning technology (e.g., Artificial Neural Network-ANN), have been widely used for water resources management including groundwater modelling [25][26][27][28][29][30]. Despite computational efficiency of metamodelling, there are limitations and challenges in its application, including high-dimensional problems, uncertainties, validation and overfitting, etc.…”