AI-Powered Generative Models for Predictive and Optimized Aquaculture Water Management

Aquaculture is one of the most important in world food security but is tremendously challenged due to water scarcity, climatic variability, and poor water quality. Sustainable water management is necessary to support the health of fish and their optimum productivity and to maintain the relationship between fish and the environment. The chapter proposes an artificial intelligence-based prediction and optimization system called Generative Artificial Intelligence (GenAI) to optimize water use in aquaculture. It is a hybrid approach that uses IoT-based real-time water quality and real-life simulations of key parameters such as dissolved oxygen (DO), pH, ammonia concentration, and turbidity, with state-of-the-art GenAI model such as Generative Adversarial Networks (GANs) and Vibrational Auto encoders (VAEs). Predictive analytics and optimization algorithms using reinforcement learning adjust dynamically exchange rates of water, aeration cycles, and nutrient loads to reduce waste of resources and enhance efficiency of the system. The suggested framework considers hydrological and climatic forecasting to predict the availability of water within a season and address the effects of extreme weather conditions, including droughts and floods. The system helps in the conservation of fresh water and also increases the performance of fish growth because it helps in achieving multi-objective optimization. An example of the application of the same technology to enhance operational sustainability and climate resilience in water-stressed areas is seen in a case study conducted on the aquaculture industry in worldwide and Bangladesh. The ethical, governance, and technical aspects of implementing AI in water management are also discussed in the chapter to achieve transparency, fairness, and environmental responsibility.