Use of data mining to establish associations between Indian marine fish catch and environmental data
DOI:
https://doi.org/10.2298/ABS230909037GKeywords:
Marine fish production, Apriori, ECLAT, FP-Growth, association rule mining (ARM)Abstract
Paper description:
- The complexity, high variability and spatio-temporal dynamics in marine fish catch composition and environmental dataset necessitate advanced data analytical approaches.
- Association rule mining algorithms (Apriori, ECLAT and FP-Growth) were used to find frequently occurring itemsets in marine fish catch and environment data of the west and east coast of India from 2011-2020.
- Linear and inverse associations were found between changes in sea temperature and chlorophyll concentration, and major catch groups (anchovies, oil sardine, Indian mackerel, hairtails, butterfish, Bombay duck, tiger prawns, cephalopods).
- Efficient association mining algorithms like FP-Growth can be used to support marine fisheries resource assessment and management strategies.
Abstract: For decades, changes in fish catch composition and the marine environment have been monitored worldwide and recorded in databases like FAO FishStatJ and the European Union Copernicus Marine Service. However, the complexity and high variability in the dataset makes it challenging to find meaningful information through conventional data analytical methods. Therefore, in this pilot data mining study, we employed association rule mining algorithms (Apriori, ECLAT, and FP-Growth) to find frequently occurring itemsets in the fish-catch composition and marine environment data of the west and east coasts of India during the past decade (2011-2020). Firstly, the inherent spatial and temporal variations in fish-catch composition and marine environment (sea surface temperature and chlorophyll) on the west and east coasts of India were statistically analyzed and described. Then, the data were preprocessed, selected, and transformed into categorical attributes. By applying the association rule mining algorithms written in the Python language in the Google Colab workspace, we obtained frequent itemsets of fish catch and marine environment with different levels of minimum support and confidence. The preliminary results showed linear and inverse associations between changes in the sea surface temperature, chlorophyll concentration, and major catch groups, such as anchovies, Indian oil sardine, Indian mackerel, hairtails, butterfish-pomfrets, Bombay duck, flatfish, tunas, giant tiger prawn, crabs, lobsters, and cephalopods. Among the tested data mining algorithms, FP-Growth was found to be more efficient and reliable in finding associations between the spatiotemporal dynamics of the marine environment and fish distribution and abundance. Therefore, it can be potentially used to support marine fisheries’ resource assessment and management strategies after refinement.
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