基于PSO算法BP神经网络的拱形温室大棚薄膜风雹耦合所致冰雹冲击力预测模型
PSO-Optimized BP Neural Network Model for Predicting Hail Impact Resulting from Wind-Hail Coupling on Greenhouse Film for Arched Greenhouses

风雹灾害是造成农业生产设施破坏和经济损失的主要自然灾害之一，因此有必要构建一个高效且准确的冰雹冲击力预测模型。本研究以拱形温室大棚薄膜风雹耦合试验为基础，采用粒子群优化(Particle Swarm Optimization, PSO)算法与反向传播(Back Propagation, BP)神经网络相结合的方法，构建一个高效且准确的冰雹冲击力预测模型。该模型的平均绝对误差为0.22929，平均偏差误差为？0.09017，确定系数为0.99704。相较于传统线性回归预测方法，该模型可处理大数据量，适应性强，拟合效果好，且避免了传统BP模型容易陷入局部最小的缺点。
Hail disasters are one of the major natural hazards causing damage to agricultural production facilities and economic losses, necessitating the development of an efficient and accurate hail impact force prediction model. This study employs a PSO-BP neural network approach, grounded in wind-hail coupling experiments on arched greenhouse films. The resultant model demonstrates superior performance with a mean absolute error (MAE) of 0.22929, a mean bias error (MBE) of ？0.09017, and a determination coefficient (R²) of 0.99704. It surpasses traditional linear regression methods in handling large datasets, adaptability, fitting accuracy, and mitigating the issue of local minima in BP models.