Simulation for the migration of Schizothorax prenanti in vertical slot fishway

Abstract: Fishway is one of the common measures to maintain the continuity of rivers and protect fish resources in the world. Among them, the hydraulic characteristics of the early fishway cannot match the swimming ability and habits of the domestic fish, particularly in the early stage of construction. Fortunately, the internal structure can be optimized to improve the efficiency of fishways operation in recent years. However, the complicated physical experiment depends greatly on seasonal, temperature, light, and external factors. Additionally, the test cost is ever increasing in a large amount of fish and feeding. Therefore, it is a high demand to predict the fish migration routes in the field of fish conservation using mathematical models. Taking Schizothorax prenanti as the research object, this study aims to simulate fish migration in the vertical slot fishway. The original data set was divided using upstream data monitored in the previous test, according to the swimming characteristics of Schizothorax prenanti. Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), and Gradient Boosting Decision Tree (GBDT) were used to establish the multiple Behavior classification model (BCM), and Swimming speed regression model (SSRM). According to the evaluation indexes of the classification and regression model, the optimal model was selected to establish the mapping relationship between the fish behaviors and hydraulic indexes using the Eulerian-Lagrangian-agent method (ELAM) framework, and then the fish migration model was constructed. The improved model also considered the influence of velocity (magnitude, vector, and velocity gradient) and inertia factors on the movement of fish. Specifically, the perception area of the fish body was represented by a two-dimensional circular plane with the inductive distance as the radius, particularly with the center of the fish body as the origin. Finally, the simulated and the observed track were compared to verify the reliability of the fish migration model. The results showed that the RF model performed the best to predict the swimming behavior classification and swimming speed regression. The classification accuracy of swimming behavior was 0.804, the recall rate was 0.794, and the F1 score was 0.798. The R2 values were all greater than 0.75 for the swimming speed regression using the three data sets. The predicted trajectories of the migration model were better agreed with the actual. Most virtual fish successively passed through the mainstream and the right backflow area, indicating better consistency with the actual fish passing experiment. A comparison was made to obtain the characteristic track of the numerical simulation and the actual fish test. Consequently, the fish migration model can be expected to fully reflect the basic behavior characteristics of the target fish, and then to better predict the migration route of Schizothorax prenanti. The finding can also provide a strong reference to evaluate the fishway flow field for the protection measures in the fish industry.