Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@Article{pourmorad:2024:AS,

• author = "Saeid Pourmorad and Mostafa Kabolizade and Luca Antonio Dimuccio",
• title = "Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review",
• journal = "Applied Sciences",
• year = "2024",
• volume = "14",
• number = "16",
• pages = "Article No. 7358",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/14/16/7358",
• DOI = "doi:10.3390/app14167358",
• abstract = "Artificial Intelligence (AI) methods, including Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs), Support Vector Machines (SVMs), Deep Learning (DL), Genetic Programming (GP) and Hybrid Algorithms, have proven to be important tools for accurate groundwater level (GWL) modelling. Through an analysis of the results obtained in numerous articles published in high-impact journals during 2001-2023, this comprehensive review examines each method's capabilities, their combinations, and critical considerations about selecting appropriate input parameters, using optimisation algorithms, and considering the natural physical conditions of the territories under investigation to improve the models' accuracy. For example, ANN takes advantage of its ability to recognise complex patterns and non-linear relationships between input and output variables. In addition, ANFIS shows potential in processing diverse environmental data and offers higher accuracy than alternative methods such as ANN, SVM, and GP. SVM excels at efficiently modelling complex relationships and heterogeneous data. Meanwhile, DL methods, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), are crucial in improving prediction accuracy at different temporal and spatial scales. GP methods have also shown promise in modelling complex and nonlinear relationships in groundwater data, providing more accurate and reliable predictions when combined with optimisation techniques and uncertainty analysis. Therefore, integrating these methods and optimisation techniques (Hybrid Algorithms), tailored to specific hydrological and hydrogeological conditions, can significantly increase the predictive capability of GWL models and improve the planning and management of water resources. These findings emphasise the importance of thoroughly understanding (a priori) the functionalities and capabilities of each potentially beneficial AI-based methodology, along with the knowledge of the physical characteristics of the territory under investigation, to optimise GWL predictive models.",
• notes = "also known as \cite{app14167358}",

}

Genetic Programming entries for Saeid Pourmorad Mostafa Kabolizade Luca Antonio Dimuccio

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