A computational intelligence framework for multimodal oceanic data analysis and predictive modeling in sustainable marine resource management

Abstract

The rapid expansion of ocean based economic activities and increasing anthropogenic pressures have made sustainable marine resource management a critical challenge in oceanic computing. Effective management of ocean ecosystems necessitates intelligent systems capable of integrating multimodal data, capturing spatiotemporal dynamics, and operating under uncertainty. Traditional approaches often rely on static optimization models or oversimplified biological assumptions, limiting their ability to address real world complexities such as ecological feedback, regulatory compliance, and conflicts among user groups. To overcome these limitations, we propose a computational intelligence framework that integrates high resolution ecological modeling with dynamic decision making and decentralized governance mechanisms. Central to this framework is the ECO STORM model (Ecologically Constrained Optimization via Stochastic Temporal Resource Management), which employs a stochastic dynamic programming approach to unify biomass evolution, habitat conditions, and enforcement factors. Neural function approximators are utilized for value iteration, while softmax parametrized policies enable adaptive effort allocation at the regional level. Complementing this, we introduce AQUA GOV (Adaptive Quota and Uncertainty Aware Governance), a multi agent governance strategy that allocates quotas under ecological and regulatory constraints, incorporating user specific preferences and compliance risks. AQUA GOV features fairness aware quota adjustments, stochastic monitoring, and a reinforcement loop that integrates stakeholder feedback. This closed loop system effectively models, forecasts, and manages marine resources under uncertainty. Experiments conducted in simulated marine environments demonstrate the framework’s ability to maintain ecological thresholds, ensure policy compliance, and optimize resource distribution among diverse user groups. This work provides a scalable and intelligent solution for advancing marine governance in data rich, heterogeneous, and ecologically sensitive seascapes.