竖缝式鱼道内齐口裂腹鱼洄游行为模拟

鱼道是帮助洄游鱼类跨越河道内物理障碍的主要过鱼设施之一。针对鱼道结构优化试验耗时长、成本高的问题。该研究在物理试验基础上建立模拟鱼类运动的数值模型，基于竖缝式鱼道内齐口裂腹鱼的行为数据，结合机器学习和欧拉-拉格朗日智能体方法(Eulerian-Lagrangian-agent method,ELAM)建立了鱼类洄游路线预测模型。首先，将齐口裂腹鱼的游泳行为数据划分为3个数据集(顺流而下、侧向运动和逆流而上)；然后，根据不同机器学习算法(XGBoost、K-近邻、梯度提升决策树、随机森林)分别构建游泳行为分类模型和游泳速度回归模型，并通过试验结果对比，筛选出最优模型；最后，结合ELAM的框架，构建鱼类洄游数学模型，模拟了齐口裂腹鱼在3种体型的竖缝式鱼道中的洄游路线。结果表明：基于随机森林建立的游泳行为分类模型和游泳速度回归模型预测效果最佳，游泳行为分类精度为0.804，召回率为0.794,F1得分为0.798,3个数据集的游泳速度回归的R²均大于0.75。研究所建立的鱼类洄游数学模型能够较好的预测齐口裂腹鱼的洄游路线，可为相关鱼类保护措施的设计和优化提供参考。

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.