Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review

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@Article{MADAENI:2020:CRST,

• author = "Fatemehalsadat Madaeni and Rachid Lhissou and Karem Chokmani and Sebastien Raymond and Yves Gauthier",
• title = "Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review",
• journal = "Cold Regions Science and Technology",
• year = "2020",
• volume = "174",
• pages = "103032",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Forecasting, Ice jam, Modelling, Neural networks, Fuzzy logic",
• ISSN = "0165-232X",
• URL = "http://www.sciencedirect.com/science/article/pii/S0165232X18304634",
• DOI = "doi:10.1016/j.coldregions.2020.103032",
• size = "37 apges",
• abstract = "In cold regions, the high occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams. These floods can be critical hydrological and hydraulic events and be a major concern for citizens, authorities, insurance companies and government agencies. In the past twenty years, several studies have been conducted in ice jam modelling and forecasting, and it has been found that predicting ice jam formation and breakup is challenging, due to the complexity of the interactions between the hydroclimatic variables leading to these processes. At this time, several mathematical models have been developed to predict breakup processes. The current methods of breakup prediction are highly empirical and site-specific. The information on the progress of the methods and the variables used to predict the occurrence, severity, and timing of the breakup ice jams still remains limited. This study summarizes the different processes contributing to ice jam formation and breakup, the various existing ice jam prediction models, and their potential and limitations regarding the improvement in ice jam predictions. An overview of the application of artificial neural networks and fuzzy logic systems in ice-related problems is presented. Genetic programming is also explained as a possible mean for ice-related problems. Although genetic programming shows promising results in hydrological modelling, it has not yet been used in ice-related problems. The review of literature highlights that data-driven and machine learning techniques provide promising means in predicting ice jams with better confidence, but more scientific research is needed",
• notes = "INRS-ETE, Universite du Quebec, 490 rue de la Couronne, Quebec G1K 9A9, Canada",

}

Genetic Programming entries for Fatemehalsadat Madaeni Rachid Lhissou Karem Chokmani Sebastien Raymond Yves Gauthier

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