Flood Forecasting Method and Application Based on Informer Model
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithm...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/5/765 |
| _version_ | 1797263799229612032 |
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| author | Yiyuan Xu Jianhui Zhao Biao Wan Jinhua Cai Jun Wan |
| author_facet | Yiyuan Xu Jianhui Zhao Biao Wan Jinhua Cai Jun Wan |
| author_sort | Yiyuan Xu |
| collection | DOAJ |
| description | Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control. |
| first_indexed | 2024-04-25T00:18:45Z |
| format | Article |
| id | doaj.art-4ecc262fb10f470ea907bb6d92cd4e13 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-25T00:18:45Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-4ecc262fb10f470ea907bb6d92cd4e132024-03-12T16:58:00ZengMDPI AGWater2073-44412024-03-0116576510.3390/w16050765Flood Forecasting Method and Application Based on Informer ModelYiyuan Xu0Jianhui Zhao1Biao Wan2Jinhua Cai3Jun Wan4School of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Water Reources and Hydropower Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electronics and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaWuhan Luoshui Intelligent Technology Co., Ltd., Wuhan 430079, ChinaFlood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.https://www.mdpi.com/2073-4441/16/5/765flood forecastingseq lengthLSTMInformer |
| spellingShingle | Yiyuan Xu Jianhui Zhao Biao Wan Jinhua Cai Jun Wan Flood Forecasting Method and Application Based on Informer Model Water flood forecasting seq length LSTM Informer |
| title | Flood Forecasting Method and Application Based on Informer Model |
| title_full | Flood Forecasting Method and Application Based on Informer Model |
| title_fullStr | Flood Forecasting Method and Application Based on Informer Model |
| title_full_unstemmed | Flood Forecasting Method and Application Based on Informer Model |
| title_short | Flood Forecasting Method and Application Based on Informer Model |
| title_sort | flood forecasting method and application based on informer model |
| topic | flood forecasting seq length LSTM Informer |
| url | https://www.mdpi.com/2073-4441/16/5/765 |
| work_keys_str_mv | AT yiyuanxu floodforecastingmethodandapplicationbasedoninformermodel AT jianhuizhao floodforecastingmethodandapplicationbasedoninformermodel AT biaowan floodforecastingmethodandapplicationbasedoninformermodel AT jinhuacai floodforecastingmethodandapplicationbasedoninformermodel AT junwan floodforecastingmethodandapplicationbasedoninformermodel |