Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia. (March 2022)

• Record Type: Journal Article
• Authors: Clark, Stephanie R.
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Hydrology -- Multiple time series -- LSTM -- Visualisation -- Clustering -- Machine learning -- Water resources -- SOM; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105295
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Understanding the sustainability of current groundwater extractions is critical in the face of changing climate and anthropogenic conditions, but this proves challenging in areas with complex, and not well understood, hydrogeology. A combination of unsupervised (self-organizing map, SOM) and supervised (long short-term memory, LSTM) models is demonstrated here to effectively abstract prevalent patterns from a diverse set of groundwater monitoring time series in the dry and hydrogeologically complicated Namoi region, enabling predictions of water levels based on climate and anthropogenic conditions to be made using a set of regional deep-learning based neural networks. By drawing on shared pattern information from across the Namoi system, the SOM reduces the complexity of the multiple time series, shares information between sparse time series which could not be modelled with the LSTM individually, adds a spatial aspect to the LSTM analysis, and provides a valuable visual analysis that enhances communication and decision-making. Highlights: Environmental data sets often have too much missing data and are too small for machine learning. Unsupervised clustering with the SOM merges timeseries into data sets suitable for use with LSTMs. The SOM provides a visual analysis of historical temporal groundwater patterns for communication. LSTMs are created that share information between multiple time series with similar temporal patterns. Predictions are enabled on time series that individually would not support LSTMs.