Improving Results of Existing Groundwater Numerical Models Using Machine Learning Techniques: A Review

• Published in: Water (Basel) Vol. 14; no. 15; p. 2307
• Main Author: Di Salvo, Cristina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Aquifers; artificial neural network; Boundary conditions; Calibration; Decision making; Decision trees; Error correction; Groundwater; Groundwater levels; Groundwater management; Learning algorithms; Machine learning; machine learning models; Mathematical models; Neural networks; Numerical models; Numerical prediction; Observation wells; Parameter estimation; Partial differential equations; Physical properties; physically-based models; Predictions; random forest; Reviews; support vector machine; Support vector machines; Surface water; Water balance
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14152307

This paper presents a review of papers specifically focused on the use of both numerical and machine learning methods for groundwater level modelling. In the reviewed papers, machine learning models (also called data-driven models) are used to improve the prediction or speed process of existing numerical modelling. When long runtimes inhibit the use of numerical models, machine learning models can be a valid alternative, capable of reducing the time for model development and calibration without sacrificing accuracy of detail in groundwater level forecasting. The results of this review highlight that machine learning models do not offer a complete representation of the physical system, such as flux estimates or total water balance and, thus, cannot be used to substitute numerical models in large study areas; however, they are affordable tools to improve predictions at specific observation wells. Numerical and machine learning models can be successfully used as complementary to each other as a powerful groundwater management tool. The machine learning techniques can be used to improve calibration of numerical models, whereas results of numerical models allow us to understand the physical system and select proper input variables for machine learning models. Machine learning models can be integrated in decision-making processes when rapid and effective solutions for groundwater management need to be considered. Finally, machine learning models are computationally efficient tools to correct head error prediction of numerical models.