Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model

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@Article{DANANDEHMEHR:2022:ecolind,

• author = "Ali {Danandeh Mehr} and Jaakko Erkinaro and Jan Hjort and Ali {Torabi Haghighi} and Amirhossein Ahrari and Maija Korpisaari and Jorma Kuusela and Brian Dempson and Hannu Marttila",
• title = "Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model",
• journal = "Ecological Indicators",
• volume = "142",
• pages = "109203",
• year = "2022",
• ISSN = "1470-160X",
• DOI = "doi:10.1016/j.ecolind.2022.109203",
• URL = "https://www.sciencedirect.com/science/article/pii/S1470160X22006756",
• keywords = "genetic algorithms, genetic programming, Ecohydrological modelling, Scarce data, Arctic Charr, Jittering",
• abstract = "Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often much smaller than hydrological observations, and ii) ecological measurements over the long-term are costly. Consequently, ecohydrological datasets are scarce and imbalanced. To address these problems, we propose jittered binary genetic programming (JBGP) to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway. We quantitatively assessed the accuracy of the proposed model and compared its performance with that of classic genetic programming (GP), decision tree (DT) and state-of-the-art jittered-DT methods. The JBGP achieves the highest total classification accuracy of 90percent and a Heidke skill score of 78percent, showing its superiority over its counterparts. Our results showed that the dominant factors contributing to the presence of Arctic charr in Teno River tributaries include i) a higher density of macroinvertebrates, ii) a lower percentage of mires in the catchment and iii) a milder stream channel slope",

}

Genetic Programming entries for Ali Danandeh Mehr Jaakko Erkinaro Jan Hjort Ali Torabi Haghighi Amirhossein Ahrari Maija Korpisaari Jorma Kuusela Brian Dempson Hannu Marttila

Citations