| 基于不同人工神经网络的水质预测方法对比 |
| 摘要点击 1626 全文点击 289 投稿时间：2023-10-11 修订日期：2024-01-16 |
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| 中文关键词 湘江流域 水质预测 机器学习 人工神经网络 模型性能 |
| 英文关键词 Xiangjiang River Basin water quality prediction machine learning artificial neural network model performance |
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| 中文摘要 |
| 利用现有水质数据对未来水质变化进行预测是实现区域规划与流域管理的有效工具．基于2022年4月至2022年5月湘江流域衡阳段水质监测数据,构建了反向传播神经网络（BPNN）与卷积神经网络（CNN）水质指标预测模型,并对高锰酸盐指数的预测结果进行了比较分析．数据显示,BPNN模型的预测值与真实水质情况基本吻合,但在拟合过程中出现了过拟合现象,利用粒子群算法（PSO）改进BPNN模型的参数选择方式,能够避免这一现象．而CNN模型拥有更复杂的结构、更科学的拟合方法,从而避免了模型在拟合过程中陷入局部极值,同时提高了模型预测结果的准确性．以不同模型的均方根误差（RMSE）、决定系数（R2）与平均绝对误差（MAE）作为评价参数,与传统的BPNN模型相比,PSO-BPNN模型中测试集的RMSE从0.278 2 mg·L-1降低到0.210 9 mg·L-1,MAE从0.222 3 mg·L-1降低到0.153 7 mg·L-1,R2从0.864 0提高到0.921 8,PSO-BPNN模型拥有更加稳定的拟合效果．CNN模型测试集的RMSE、MAE和R2分别为0.122 0 mg·L-1、0.092 7 mg·L-1和0.970 5,显示CNN模型预测效果更好． |
| 英文摘要 |
| The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network （BPNN） and convolutional neural network （CNN） were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality； however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization （PSO） could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error （RMSE）, coefficient of determination （R2）, and mean absolute error （MAE） were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L-1 to 0.210 9 mg·L-1, reduced the MAE of the test set from 0.222 3 mg·L-1 to 0.153 7 mg·L-1 and increased the R2 of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and R2 of the test set in the CNN model were 0.122 0 mg·L-1, 0.092 7 mg·L-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN. |
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