A forecasting method of optimal search area for Ulva prolifera green tide

Since 2008, The on-site salvage of floating Ulva prolifera patches at sea has been a crucial measure in mitigating the adverse effects of the Yellow Sea green tide. A timely and accurate identification of the search area is crucial for salvage boats to handle reported floating U. prolifera patches effectively. Traditional deterministic drift forecasting methods do not adequately address the uncertain behavior of floating U. prolifera patches. This inadequacy arises from various environmental and biological complexities. In this study, we proposed a Monte Carlo probabilistic drift forecasting model by introducing random walk, and constructed a search area forecasting method based on optimal search theory. This method was applied to the on-site salvage of floating U. prolifera patches in June 2023. The optimal search area is more efficient and effective than traditional methods, reducing the search area by more than an order of magnitude. This method offers decision-makers valuable additional information, such as probability distribution of the search area, thereby enhancing salvage efficiency and ultimately mitigating resource waste. • Traditional forecasting methods ignore the uncertain behavior of floating Ulva prolifera patches. • A Monte Carlo probabilistic drift forecasting model is proposed for optimal search area. • The model yields more efficient and effective search area than traditional ones.