Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network
High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/22/3683 |
| _version_ | 1797463759059419136 |
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| author | Xinyu Yang Xiao Zhang Jiancang Xie Xu Zhang Shihui Liu |
| author_facet | Xinyu Yang Xiao Zhang Jiancang Xie Xu Zhang Shihui Liu |
| author_sort | Xinyu Yang |
| collection | DOAJ |
| description | High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction. |
| first_indexed | 2024-03-09T17:56:15Z |
| format | Article |
| id | doaj.art-74c06f7e63a14990b29511ed666b631d |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T17:56:15Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-74c06f7e63a14990b29511ed666b631d2023-11-24T10:21:16ZengMDPI AGWater2073-44412022-11-011422368310.3390/w14223683Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural NetworkXinyu Yang0Xiao Zhang1Jiancang Xie2Xu Zhang3Shihui Liu4State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaState Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaState Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaState Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaState Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaHigh-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction.https://www.mdpi.com/2073-4441/14/22/3683fuzzy information granulationgenetic algorithminterval predictionneural networksWei River |
| spellingShingle | Xinyu Yang Xiao Zhang Jiancang Xie Xu Zhang Shihui Liu Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network Water fuzzy information granulation genetic algorithm interval prediction neural networks Wei River |
| title | Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network |
| title_full | Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network |
| title_fullStr | Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network |
| title_full_unstemmed | Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network |
| title_short | Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network |
| title_sort | monthly runoff interval prediction based on fuzzy information granulation and improved neural network |
| topic | fuzzy information granulation genetic algorithm interval prediction neural networks Wei River |
| url | https://www.mdpi.com/2073-4441/14/22/3683 |
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