| 郝玉莹， 赵 林， 孙 同， 乔 治.基于RF-LSTM的地表水体水质预测Journal of Water Resources and Water Engineering[J].,2021,32(6):41-48 |
| 基于RF-LSTM的地表水体水质预测 |
| Surface water quality prediction based on RF-LSTM |
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| DOI：10.11705/j.issn.1672-643X.2021.06.06 |
| 中文关键词: 水质预测 长短时记忆神经网络 随机森林 特征选择 |
| 英文关键词: water quality prediction long short-term memory (LSTM) neural network random forest (RF) feature selection |
| 基金项目:天津市科技重大专项与工程项目（18ZXSZSF00240） |
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| 中文摘要: |
| 水质预测是水污染防治的重要一环，为提高水质预测的精度，研究随机森林算法（RF）与长短时记忆神经网络（LSTM）相结合的预测方法。以桃林口水库水质监测数据为例，采用RF算法分别筛选出影响高锰酸盐指数（CODMn）、氨氮（NH3—N）、总氮（TN）和总磷（TP）浓度变化的关键特征，在此基础上构建基于RF-LSTM的水质预测模型，并与单一LSTM、RF-BPNN和RF-RNN模型的预测效果进行对比。结果表明：RF-LSTM模型的预测效果均优于其他模型，预测CODMn、NH3—N、TN和TP未来4 h浓度时的决定系数（R2）分别达到0.986、0.990、0.989和0.988，具有极高的预测精度和较强的泛化能力。研究结果为实现高精度水质预测提供了新思路。 |
| 英文摘要: |
| Water quality prediction plays an important role in the prevention and control of water pollution. In order to improve the accuracy of water quality prediction, a new prediction method based on the combination of random forest (RF) algorithm and long short-term memory (LSTM) neural network was proposed. Taking the water quality monitoring data of Taolinkou Reservoir as an example, the RF algorithm was used to screen out the key characteristics that affect the changes in the concentration of permanganate index (CODMn), ammonia nitrogen (NH3—N), total nitrogen (TN) and total phosphorus (TP). On this basis, a water quality prediction model based on RF-LSTM was constructed and the prediction results were then compared with those of sole LSTM, RF-BPNN, and RF-RNN model. The results show that the prediction performance of the RF-LSTM model is better than the other models with extremely high prediction accuracy and strong generalization ability, the coefficient of determination (R2) for predicting the concentration of CODMn, NH3—N, TN and TP in the next four hours are 0.986, 0.990, 0.989 and 0.988, respectively. The research results can provide a new approach for high-precision water quality prediction. |
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