Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model
To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multip...
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Editorial Office of Pearl River
2021-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.03.007 |
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| author | HU Shunqiang CUI Dongwen |
| author_facet | HU Shunqiang CUI Dongwen |
| author_sort | HU Shunqiang |
| collection | DOAJ |
| description | To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction. |
| format | Article |
| id | doaj-art-f3c43ce7b2d5444ab2355c4247b05e32 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-f3c43ce7b2d5444ab2355c4247b05e322025-01-15T02:29:48ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247648858Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS ModelHU ShunqiangCUI DongwenTo improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),decomposes the original runoff sequence into multiple regular component sequences through EMD,and reconstructs the phase space of each component sequence by the autocorrelation function method (AFM) and the false nearest neighbor method (FNN) to determine the input and output vectors,establishes the EMD-LSTM-ANFIS prediction model,and constructs the EMD-LSTM,EMD-ANFIS,LSTM,ANFIS as comparison models,as well as predicts and compares the annual runoff of the Longtan Station in Yunnan Province by the five models.The results show that the average relative error of the EMD-LSTM-ANFIS model for the annual runoff prediction is 3.18%,which is reduced by 55.0%、65.2%、68.1%、78.4% compared with the EMD-LSTM,EMD-ANFIS,LSTM,and ANFIS models respectively,with higher prediction accuracy and stronger generalization ability.Therefore,the EMD-LSTM-ANFIS model is feasible and reliable for runoff prediction.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.03.007runoff predictionempirical mode decompositionlong-short term memory neural networkadaptive neuro-fuzzy inference systemphase space reconstruction |
| spellingShingle | HU Shunqiang CUI Dongwen Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model Renmin Zhujiang runoff prediction empirical mode decomposition long-short term memory neural network adaptive neuro-fuzzy inference system phase space reconstruction |
| title | Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model |
| title_full | Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model |
| title_fullStr | Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model |
| title_full_unstemmed | Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model |
| title_short | Research on Annual Runoff Prediction Based on EMD-LSTM-ANFIS Model |
| title_sort | research on annual runoff prediction based on emd lstm anfis model |
| topic | runoff prediction empirical mode decomposition long-short term memory neural network adaptive neuro-fuzzy inference system phase space reconstruction |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.03.007 |
| work_keys_str_mv | AT hushunqiang researchonannualrunoffpredictionbasedonemdlstmanfismodel AT cuidongwen researchonannualrunoffpredictionbasedonemdlstmanfismodel |