Machine Learning Applications in Soil Water and Groundwater Assessment

Dear Colleagues,

Water resources play a fundamental role in social, industrial, environmental, and agricultural sectors. Consequently, the assessment of water resources encompasses a wide range of areas, as exemplified above. Currently, a multitude of operators, professionals, technicians, scholars, and researchers are actively engaged in various capacities in activities related to water resource assessment. To enhance the accuracy of these assessments, increasingly powerful models have been implemented. Additionally, secondary information, known as covariates, with even greater descriptive capabilities have been discovered and applied in this field. These covariates prove to be essential for improving the accuracy and reliability of predictions and analyses. The aim of this Special Issue is to provide a comprehensive overview of the state-of-the-art in machine learning models for soil water and groundwater prediction, painting a vivid and complete picture for scholars and professionals. A new wave of models has emerged on the scene, including INLA (Integrated Nested Laplace Approximations), GAM (Generalized Additive Models), MARS (Multivariate Adaptive Regression Splines), GPSVC (spatially varying coefficient models based on Gaussian processes), and many others. These cutting-edge models are capable of enhancing the accuracy of their predictions, and professionals are eager to learn their usage and apply them to their datasets. Each model brings along a unique set of capabilities and advantages, enabling the tackling of the complex dynamics of water resources with unprecedented accuracy. For example, INLA provides an efficient approach for analyzing complex hierarchical models, while GAMs offer flexibility in modelling nonlinear relationships. Furthermore, the use of MARS enables the identification of interactions and nonlinearities in the data, thus improving the understanding of variables influencing water resources. GPSVC models, on the other hand, allow for capturing spatial variations in data, which is crucial for large-scale water resource analysis. The continued evolution of these models represents a promising frontier for research and practice in water resource management. With the adoption of such advanced techniques, a future is envisioned where decisions regarding water resource management will increasingly be informed and based on high-quality data, ensuring sustainable and effective water resource management on a global scale.

This Special Issue would like to explore and showcase the diverse ways in which machine learning techniques are being utilized to improve the assessment, monitoring, and management of soil water and groundwater resources. This could include, but is not limited to, applications such as the predictive modelling of groundwater levels, soil moisture content estimation, contamination detection and remediation, optimization of irrigation practises, and the development of decision support systems for sustainable water resource management. Our aim would is to highlight the innovative approaches and advancements in utilizing machine learning in this critical field, ultimately contributing to the better understanding and management of water resources for agricultural, environmental, and societal benefits.

There are numerous potential topics that can be intertwined with this proposed theme, such as the following:

1. Predictive modelling of groundwater levels and quality using machine learning algorithms.
2. Estimation and monitoring of soil moisture content at various spatial and temporal scales.
3. Detection and prediction of groundwater contamination using advanced machine learning techniques.
4. Optimization of irrigation scheduling and water use efficiency through machine learning-based decision support systems.
5. Development of sensor networks and data-fusion approaches for real-time monitoring soil water and groundwater dynamics.
6. The best use of geophysical covariates to assess the soil water content.
7. Integration of remote sensing data with machine learning for improved characterization of hydrological processes.
8. Assessment of climate change’s impacts on soil water and groundwater resources using machine learning-driven modelling frameworks.
9. Risk assessment and management of groundwater-related hazards, such as droughts, floods, and land subsidence, using predictive analytics.
10. Application of machine learning in groundwater remediation strategies, including source identification, plume delineation, and remediation technology selection.
11. Development of user-friendly software tools and platforms for stakeholders involved in soil water and groundwater management, incorporating machine learning capabilities.
12. In the study of hydrological balance, the evaluation of certain terms within the balance equation, such as runoff, is crucial.
13. Evaluation of predictive uncertainty: assessing and improving, with respect to currently used approaches, the full predictive probability distributions—i.e., the probabilities of future outcomes conditional on all available information—to be effectively used within Bayesian decision-making schemes.

The scope of this Special Issue aims to cover a broad range of topics related to the application of machine learning in soil water and groundwater assessments, addressing both theoretical advancements and practical implementations in the field.

We look forward to receiving your contributions.

Dr. Emanuele Barca
Dr. Minxue He
Guest Editors

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• groundwater level
• soil moisture
• groundwater contamination
• geo-radar supporting information
• water balance equation
• machine learning
• climate change
• groundwater-related hazards
• spatial water resource mapping