Climate-Resilient Smart Aquaculture Platform Using Hybrid Renewable Energy and AI-Based Environmental Forecasting

Abstract

The impacts of climate change on aquaculture systems include changes to water quality, fish health, and energy use. Traditional monitoring and environmental control methods are often ineffective in adapting to changing climate conditions and are not predictive. This research introduces a climate-resilient, smart aquaculture system that combines Internet of Things (IoT) technology for environmental monitoring, hybrid renewable energy management, and Long Short-Term Memory (LSTM) for forecasting, to achieve sustainable aquaculture practices. This proposed system will continuously monitor water-quality parameters such as temperature, pH, dissolved oxygen, turbidity, and salinity through the use of distributed sensors via an IoT communication system. Hybrid renewable energy subsystems with solar and wind power and battery storage provide sustainable and continuous operation. The LSTM forecasting model forecasts the fluctuations in the environment and allows for intelligent management of aquaculture operations and decision making, based on the forecast. Experimental assessment has shown that the forecasting accuracy is advanced and its energy efficiency and environmental monitoring stability are superior to the conventional methods. The proposed framework is designed to make the fishery more resilient to climate change, less reliant on traditional energy resources, and to foster sustainable development of the smart fishery based on adaptive monitoring and environmental forecasting tools based on Artificial Intelligence (AI).