NPZfc: An ecological relation-based fish catch prediction model using Artificial Neural Network

Mukherjee, Manasi and Suresh, V V R and Kundu, Suman (2025) NPZfc: An ecological relation-based fish catch prediction model using Artificial Neural Network. Journal of Marine Biological Association of India, 67 (1). pp. 68-75. ISSN 2321-7898

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Abstract

Quantifying interactions of organisms of the various trophic levels is important in understanding the dynamics of aquatic ecosystems. Concerning fish, as both ecologically and commercially important components of natural aquatic ecosystems, predicting their catch in relation to primary producers provides insight into sustainable management. This paper describes a novel model NPZfc, for encompassing nutrients, phytoplankton, zooplankton and fish, which can predict planktivorous fish catch. Unlike the existing models, which deal with the interactions within the system through mathematical equilibrium, the proposed model uses an artificial neural network (ANN) to automatically learn inter-dependencies between different related variables and predict the fish catch of a water body using a limited dataset. The efficiency of the model was enhanced by refining the input variables. Here biomass of plankton species population (phytoplankton and zooplankton) was specifically selected from feeding ecology studies of target fish species as input variable. The study involving two of the commercially important fish species, Etroplus suratensis and Nematalosa nasus, in Chilika lagoon showed that the model can predict with high accuracy from limited input data. The root mean square error (RMSE) is satisfactory, ranging from 12.55 t for N. nasus to 16.13 t for E. suratensis. Higher accuracy and better predictive ability with a smaller dataset make this ANN-

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