Neuro-computational surrogates for aqueous fractional-order nekton-plankton spatiotemporal dynamics under toxicant stress, refuge efficacy, and nutrient flux modulation.
Journal:
Water research
Published Date:
Oct 28, 2025
Abstract
In aquatic ecosystems, the triadic relationship among phytoplankton, zooplankton, and their piscine predators constitutes a delicately balanced ecological continuum where nutrient cycling, toxin transfer, and spatial refugia collectively dictate population persistence and the wider vitality of marine communities. In this paper, the fractional-order dynamics of Nekton-Plankton Coupled (N-PC) systems are analyzed using intelligent computing driven autoregressive exogenous artificial neural networks (ARX-ANNs). Initially, fractional N-PC dynamics under toxicity, refuge and nutrient flux constrains are time-evolved using an efficient Adams-Moulton-Bashforth predictor corrector method incorporating the Caputo fractional operator. These diverse scenarios are subsequently fed into the ARX-ANNs that are efficiently trained by means of a hybrid second order Bayesian Regularized Levenberg Marquardt optimization algorithm. The efficiency of the intelligent computing regime in modeling and forecasting the complex dynamics of the N-PC model is comprehensively evaluated against reference simulations using mean squared error convergence curves, time-series response analysis, correlation charts (for autoregressive models), error frequency distributions, and regression reports. Comparative analysis between the traditional numerical approach against the ARX-ANNs demonstrates remarkably low errors in the ranges of 10-04 to 10-12. To further assess the robustness of the proposed methodology, step ahead forecasts of single and multiple prediction horizons are explored, and validated with low errors on the order of <10-13. Results show that the intelligent neuro-computing surrogate can effectively delineate the ecological spatiotemporal dynamics and population trajectories in complex marine food webs.
