|Alternative Title||The Runoff Forecasting of Heihe River Based on SARIMA and SVR Hybrid Model|
|Place of Conferral||兰州|
|Keyword||径流预测 时间序列分析 支持向量回归 随机森林 模拟退火智能算法|
本文采用数据驱动模型对黑河径流量进行预测，所建立的混合模型SARIMA-RandomForest-SVR能够达到良好的预测效果，拟合优度达到0.832。建模思路如下：首先采用时间序列分析的方法，建立SARIMA模型对月度径流数据进行预测。然后在残差处理的过程中，运用有监督学习的模式，以滞后期（滞后期从1到37）的残差值，滞后期残差值的滑动平均值和滑动标准差共41个变量作为候选输入变量，现期的残差值为输出变量建立支持向量回归模型。为了提高模型的效率和精度，采用随机森林算法（RandomForest）进行特征选择，对候选输入变量进行分析，从而筛选出重要性得分较高的20个因子作为模型最终的输入变量。之后，建立支持向量回归（Support Vector Regression）模型拟合残差序列，该过程中使用模拟退火智能算法（Simulated Annealing）进行参数寻优。最后将残差的预测值与时间序列模型的预测值进行整合，得到最终的预测结果。与单一模型及其他模型组合对比可以发现，本文建立的混合模型SARIMA-RandomForest-SVR能够充分利用各单一算法模型的优势，达到良好的预测效果。
The runoff forecasting is an important direction of hydrological research. It refers to the research and prediction of the runoff trends of rivers through the establishment of mathematical models. The forecast results can be widely used in flood control, drought prevention, environmental improvement, reservoir dispatch, hydropower station operation, shipping management, water resources allocation and management. However, due to various factors such as the weather system, basin surface, and human activities, the dynamics of the hydrological system have been strengthened, and its scale and complexity have increased. This poses a great challenge to the research. Runoff prediction methods can be divided into two categories: first, causal models, which are modeled according to the physical mechanisms of hydrological processes; second, data-driven models which do not consider the mechanism of runoff formation are modeled by mining the inherent changes in data. The causal model has great limitations. It does not only require detailed research on the hydrological process, but also require specific models under different conditions. It also needs high requirement for the data acquisition and research experience. Driven by the rapid development of artificial intelligence and machine learning technology, the data-driven model has become a research hotspot in recent years. It has the advantages of simplicity, good prediction accuracy, and wide applicability.
This paper uses data-driven models to predict the runoff of the Heihe River. The hybrid model SARIMA-RandomForest-SVR with better accuracy is established in the end. Its goodness-of-fit is 0.832. Firstly, the time series analysis method was used to establish the SARIMA model to forecast the monthly runoff data. Then, in the process of residual processing, a mode of supervised learning is used. A total of 41 variables including lagged period values (lag time from 1 to 37), a sliding average of the residual values of the lag period, and a sliding standard deviation are used as candidate input variables. The current residual value is used as output variable in the establishment of support vector regression model. In order to improve the efficiency and accuracy of the model, random forest algorithm was used to select features. And then the candidate input variables were analyzed. Thus, 20 factors with higher importance scores were selected as the final input variables of the model. Afterwards, a Support Vector Regression (SVR) model was built to fit the residual sequence. In this process, simulated annealing algorithm was used to perform parameter optimization. Finally, the predicted values of the residual were integrated with the predicted values of the time series model to obtain the final forecast result. Compared with the single model and other hybrid models, we can find that the hybrid model SARIMA-RandomForest-SVR established in this paper can make full use of the advantages of each single algorithm to achieve a good prediction effect.
|雷昌宁. 基于SARIMA和SVR混合模型的黑河径流量预测分析[D]. 兰州. 兰州大学,2018.|
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