Artificial Intelligence Applications in Groundwater Quality Modeling
Summary
Artificial intelligence (AI) techniques have become integral to modelling groundwater quality by capturing nonlinear relationships among hydrochemical parameters, spatial variability and anthropogenic pressures. Machine learning algorithms such as neural networks, support vector machines and ensemble methods have been applied to predict concentrations of key indicators including salinity, nitrates and trace metals. Hybrid frameworks that combine optimisation algorithms with neural networks or geostatistical interpolation have significantly improved predictive accuracy and spatial mapping. These models can integrate in situ sensor data, remote sensing inputs and land-use information to generate realistic simulations under diverse hydrogeological conditions. By enabling accurate, cost-effective monitoring and scenario analysis, AI-driven tools support proactive groundwater management strategies and policy decisions aimed at safeguarding water resources in the face of global change.
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Artificial Intelligence Applications in Groundwater Quality Modeling publication trend
The graph below shows the total number of articles in artificial intelligence applications in groundwater quality modeling across all publications each year (not limited to Nature Index journals).
Technical terms
Artificial intelligence (AI): Computational techniques that enable machines to perform tasks requiring human-like intelligence, such as pattern recognition and predictive modelling.
Machine learning (ML): A subset of AI that uses statistical algorithms to learn relationships from data and make predictions without explicit programming.
Neural network: A computational model inspired by biological neural networks, comprising interconnected processing nodes that can approximate complex nonlinear functions.
Hybrid model: An approach combining two or more algorithms or methodologies (for example, neural networks with optimisation or geostatistical methods) to enhance predictive performance.
Empirical Bayesian Kriging: A geostatistical interpolation technique that estimates spatial variables by optimising variogram parameters using Bayesian inference for improved accuracy.
Particle swarm optimisation (PSO): A global optimisation algorithm inspired by social behaviour in animals, used to tune model parameters by iteratively improving candidate solutions.
References
- Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios. PLOS ONE (2021).
- Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review. Water (2023).
- A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines. Toxics (2021).
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