Runoff prediction of urban stream based on the discharge of pump stations using improved multi-layer perceptron applying new optimizers combined with a harmony search
•An improved multi-layer perceptron using new optimizers combined with a meta-heuristic optimization algorithm (IMLP) was developed.•Runoff Inflow was predicted based on the rainfall and discharge of pump stations using IMLP.•Normalization and correlation analysis was applied to conduct data pre-pro...
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| Veröffentlicht in: | Journal of hydrology (Amsterdam) Jg. 615; S. 128708 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.12.2022
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| ISSN: | 0022-1694, 1879-2707 |
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| Abstract | •An improved multi-layer perceptron using new optimizers combined with a meta-heuristic optimization algorithm (IMLP) was developed.•Runoff Inflow was predicted based on the rainfall and discharge of pump stations using IMLP.•Normalization and correlation analysis was applied to conduct data pre-processing.•Root mean square error (RMSE) and mean average error (MAE) were applied to calculate the accuracy of the predicted runoff.•Historical rainfall events from 2010 to 2020 were used to compare MLP with IMLP.
Prediction using neural networks is applied in various fields, including hydrology and water resource management. In the learning process, an optimizer is used to determine the relationship (weights and biases) between the input and output data to lower the value of the loss function. Existing optimizers rely absolutely on the relationship that are initially generated and have the probability to converge to a local optimum. Additionally, since there is no structure for storing information about the previously generated correlation, there is a probability that the optimal weights and biases could not be found even when learning proceeds. It is necessary to apply a meta-heuristic optimization algorithm that can consider both global/local search and has a structure that can store the previously generated solution. In this study, a new optimizer was combined with a harmony search (HS), to improve existing optimizers. To test the performance of the improved multi-layer perceptron using the new optimizer (IMLP), the runoff of the Dorim stream in Seoul was predicted. The rainfall and the discharge of pump stations were constructed as input data, and data preprocessing was performed by normalization and correlation coefficients. The IMLP showed improved accuracy and could be used to manage urban streams. |
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| AbstractList | Prediction using neural networks is applied in various fields, including hydrology and water resource management. In the learning process, an optimizer is used to determine the relationship (weights and biases) between the input and output data to lower the value of the loss function. Existing optimizers rely absolutely on the relationship that are initially generated and have the probability to converge to a local optimum. Additionally, since there is no structure for storing information about the previously generated correlation, there is a probability that the optimal weights and biases could not be found even when learning proceeds. It is necessary to apply a meta-heuristic optimization algorithm that can consider both global/local search and has a structure that can store the previously generated solution. In this study, a new optimizer was combined with a harmony search (HS), to improve existing optimizers. To test the performance of the improved multi-layer perceptron using the new optimizer (IMLP), the runoff of the Dorim stream in Seoul was predicted. The rainfall and the discharge of pump stations were constructed as input data, and data preprocessing was performed by normalization and correlation coefficients. The IMLP showed improved accuracy and could be used to manage urban streams. •An improved multi-layer perceptron using new optimizers combined with a meta-heuristic optimization algorithm (IMLP) was developed.•Runoff Inflow was predicted based on the rainfall and discharge of pump stations using IMLP.•Normalization and correlation analysis was applied to conduct data pre-processing.•Root mean square error (RMSE) and mean average error (MAE) were applied to calculate the accuracy of the predicted runoff.•Historical rainfall events from 2010 to 2020 were used to compare MLP with IMLP. Prediction using neural networks is applied in various fields, including hydrology and water resource management. In the learning process, an optimizer is used to determine the relationship (weights and biases) between the input and output data to lower the value of the loss function. Existing optimizers rely absolutely on the relationship that are initially generated and have the probability to converge to a local optimum. Additionally, since there is no structure for storing information about the previously generated correlation, there is a probability that the optimal weights and biases could not be found even when learning proceeds. It is necessary to apply a meta-heuristic optimization algorithm that can consider both global/local search and has a structure that can store the previously generated solution. In this study, a new optimizer was combined with a harmony search (HS), to improve existing optimizers. To test the performance of the improved multi-layer perceptron using the new optimizer (IMLP), the runoff of the Dorim stream in Seoul was predicted. The rainfall and the discharge of pump stations were constructed as input data, and data preprocessing was performed by normalization and correlation coefficients. The IMLP showed improved accuracy and could be used to manage urban streams. |
| ArticleNumber | 128708 |
| Author | Lee, Eui Hoon |
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| Cites_doi | 10.1109/ACCESS.2020.2974406 10.1016/j.jhydrol.2020.124710 10.1023/A:1022602019183 10.15244/pjoes/81557 10.1007/s11269-019-2183-x 10.1016/j.jhydrol.2020.124631 10.1016/j.jhydrol.2019.05.087 10.1029/2018WR024463 10.1007/s00477-018-1560-y 10.1088/1748-9326/ab4d5e 10.1080/02626667.2019.1595624 10.3390/w14010099 10.1016/j.eswa.2008.05.024 10.1037/h0042519 10.1016/j.jhydrol.2018.08.050 10.1007/s00477-020-01766-4 10.18178/ijesd.2019.10.10.1190 10.1029/2019WR025326 10.5194/hess-22-6005-2018 10.1016/j.engappai.2015.07.019 10.1016/j.engappai.2012.05.023 10.1177/003754970107600201 10.2166/wcc.2017.307 10.1016/j.jhydrol.2020.124776 10.1016/j.jhydrol.2020.124783 |
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| Keywords | Meta-heuristic optimization algorithm Urban stream Harmony search Runoff prediction Combined optimizer Improved multi-layer perceptron |
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| SubjectTerms | algorithms Combined optimizer Harmony search Improved multi-layer perceptron Meta-heuristic optimization algorithm prediction probability rain runoff Runoff prediction streams Urban stream water management |
| Title | Runoff prediction of urban stream based on the discharge of pump stations using improved multi-layer perceptron applying new optimizers combined with a harmony search |
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