Weighted Combined Water Level Prediction Based on Nonlinear Programming Genetic Algorithm
A single prediction method has its own advantages and disadvantages in different aspects. In order to improve the accuracy of water level prediction, a water level prediction method combining The Autoregressive Integrated Moving Average (ARIMA) Model, Exponential Smoothing (ES) model and Long Short-...
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| Vydáno v: | 2022 7th International Conference on Computational Intelligence and Applications (ICCIA) s. 140 - 145 |
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24.06.2022
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| Abstract | A single prediction method has its own advantages and disadvantages in different aspects. In order to improve the accuracy of water level prediction, a water level prediction method combining The Autoregressive Integrated Moving Average (ARIMA) Model, Exponential Smoothing (ES) model and Long Short-term Memory (LSTM) model through nonlinear programming genetic algorithm is proposed in this paper. By combining the advantages of local search of nonlinear programming and global search of genetic algorithm, this method uses nonlinear programming genetic algorithm to allocate the weights of ARIMA model, ES model and LSTM model, and obtains the final water level prediction result by weighting. The empirical results show that this method not only has higher prediction accuracy than single model, but also has higher prediction accuracy than using nonlinear programming or genetic algorithm to allocate weight. |
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| AbstractList | A single prediction method has its own advantages and disadvantages in different aspects. In order to improve the accuracy of water level prediction, a water level prediction method combining The Autoregressive Integrated Moving Average (ARIMA) Model, Exponential Smoothing (ES) model and Long Short-term Memory (LSTM) model through nonlinear programming genetic algorithm is proposed in this paper. By combining the advantages of local search of nonlinear programming and global search of genetic algorithm, this method uses nonlinear programming genetic algorithm to allocate the weights of ARIMA model, ES model and LSTM model, and obtains the final water level prediction result by weighting. The empirical results show that this method not only has higher prediction accuracy than single model, but also has higher prediction accuracy than using nonlinear programming or genetic algorithm to allocate weight. |
| Author | Wang, Congyou Jia, Lu Cuan, Wanbing |
| Author_xml | – sequence: 1 givenname: Congyou surname: Wang fullname: Wang, Congyou email: 408371679@qq.com organization: The Eastern Route Of South-to-North Water, Diversion Project Jiangsu Water Source Co.,Ltd,Nanjing,China – sequence: 2 givenname: Wanbing surname: Cuan fullname: Cuan, Wanbing email: 1205428937@qq.com organization: The Eastern Route Of South-to-North Water, Diversion Project Jiangsu Water Source Co.,Ltd,Nanjing,China – sequence: 3 givenname: Lu surname: Jia fullname: Jia, Lu email: 824056498@qq.com organization: School of Physical and Mathematical Science, Nanjing Tech University,Nanjing,China |
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| Snippet | A single prediction method has its own advantages and disadvantages in different aspects. In order to improve the accuracy of water level prediction, a water... |
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| SubjectTerms | Aerospace electronics combined water level prediction Computational modeling Lakes nonlinear programming genetic algorithm Predictive models Programming Resource management Smoothing methods weighted combination |
| Title | Weighted Combined Water Level Prediction Based on Nonlinear Programming Genetic Algorithm |
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