Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network
The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valu...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/5358915 |
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| _version_ | 1832559617175977984 |
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| author | Ziwei Yu Jinhuang Yu Jinjie Liu Chenglong Hu Shengsheng Hu Junjie Wang Hehe Zhang Huiting Lu |
| author_facet | Ziwei Yu Jinhuang Yu Jinjie Liu Chenglong Hu Shengsheng Hu Junjie Wang Hehe Zhang Huiting Lu |
| author_sort | Ziwei Yu |
| collection | DOAJ |
| description | The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations. |
| format | Article |
| id | doaj-art-3f50f16816d74d36995b0b9af1c0cbaf |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3f50f16816d74d36995b0b9af1c0cbaf2025-02-03T01:29:32ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/5358915Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural NetworkZiwei Yu0Jinhuang Yu1Jinjie Liu2Chenglong Hu3Shengsheng Hu4Junjie Wang5Hehe Zhang6Huiting Lu7College of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringCollege of Civil EngineeringThe pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations.http://dx.doi.org/10.1155/2024/5358915 |
| spellingShingle | Ziwei Yu Jinhuang Yu Jinjie Liu Chenglong Hu Shengsheng Hu Junjie Wang Hehe Zhang Huiting Lu Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network Advances in Civil Engineering |
| title | Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network |
| title_full | Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network |
| title_fullStr | Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network |
| title_full_unstemmed | Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network |
| title_short | Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network |
| title_sort | prediction model for safe operation of pumping stations optimized by the sparrow search algorithm and bp neural network |
| url | http://dx.doi.org/10.1155/2024/5358915 |
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