Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province
It is significant to achieve the scientific forecast and quantitative analysis of construction output. In most existing construction economic forecasting methods, both time series models and BP neural network fail to consider the change in relevant influencing factors. This paper introduced the supp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/1/48 |
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| author | Ming Lei Yuejie He Dandan Wang Debin He Yuhao Feng Lianhuan Cheng Zihao Qin |
| author_facet | Ming Lei Yuejie He Dandan Wang Debin He Yuhao Feng Lianhuan Cheng Zihao Qin |
| author_sort | Ming Lei |
| collection | DOAJ |
| description | It is significant to achieve the scientific forecast and quantitative analysis of construction output. In most existing construction economic forecasting methods, both time series models and BP neural network fail to consider the change in relevant influencing factors. This paper introduced the support vector machine (SVM) to solve the above problems based on the grid search method (GSM) optimization model. First, based on constructing an index system of influencing factors of the gross industrial output, a grey relational method is adopted to verify the correlation between the eight factors and output. Furthermore, a SVM forecast model of the gross output is constructed with the relative datasets and influencing factors of the construction industry in Hubei from 2001 to 2016 as a training sample, while the parameters are optimized using the GSM. Then, the model is used to forecast and analyze the gross output from 2017 to 2020 while checking errors. Finally, according to systematic comparison analyses among three forecast models, including the GSM-SVM model, BP neural network, and grey GM (1,1), the results showed that the GSM-SVM forecast model processed the higher solution accuracy and generalization ability. The effectiveness and reliability of our proposed model in the field of construction output forecasting are verified. It can provide a more effective modeling and forecasting method for the gross output value of the construction industry. |
| first_indexed | 2024-03-09T13:21:52Z |
| format | Article |
| id | doaj.art-e838333d2ec1477bae210634256268fd |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T13:21:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-e838333d2ec1477bae210634256268fd2023-11-30T21:29:09ZengMDPI AGBuildings2075-53092022-12-011314810.3390/buildings13010048Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei ProvinceMing Lei0Yuejie He1Dandan Wang2Debin He3Yuhao Feng4Lianhuan Cheng5Zihao Qin6School of Urban Construction, Yangtze University, Jingzhou 434023, ChinaSchool of Urban Construction, Yangtze University, Jingzhou 434023, ChinaSchool of Economics and Management, Yangtze University, Jingzhou 434023, ChinaHunan Construction Engineering Real Estate Investment Co., Ltd., Changsha 410026, ChinaSchool of Urban Construction, Yangtze University, Jingzhou 434023, ChinaSchool of Architecture and Planning, Hunan University, Changsha 410082, ChinaPowerChina Sichuan Electric Power Engineering Co., Ltd., Chengdu 610041, ChinaIt is significant to achieve the scientific forecast and quantitative analysis of construction output. In most existing construction economic forecasting methods, both time series models and BP neural network fail to consider the change in relevant influencing factors. This paper introduced the support vector machine (SVM) to solve the above problems based on the grid search method (GSM) optimization model. First, based on constructing an index system of influencing factors of the gross industrial output, a grey relational method is adopted to verify the correlation between the eight factors and output. Furthermore, a SVM forecast model of the gross output is constructed with the relative datasets and influencing factors of the construction industry in Hubei from 2001 to 2016 as a training sample, while the parameters are optimized using the GSM. Then, the model is used to forecast and analyze the gross output from 2017 to 2020 while checking errors. Finally, according to systematic comparison analyses among three forecast models, including the GSM-SVM model, BP neural network, and grey GM (1,1), the results showed that the GSM-SVM forecast model processed the higher solution accuracy and generalization ability. The effectiveness and reliability of our proposed model in the field of construction output forecasting are verified. It can provide a more effective modeling and forecasting method for the gross output value of the construction industry.https://www.mdpi.com/2075-5309/13/1/48construction outputsupport vector machineeconomic growthforecast model |
| spellingShingle | Ming Lei Yuejie He Dandan Wang Debin He Yuhao Feng Lianhuan Cheng Zihao Qin Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province Buildings construction output support vector machine economic growth forecast model |
| title | Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province |
| title_full | Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province |
| title_fullStr | Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province |
| title_full_unstemmed | Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province |
| title_short | Application of GSM-SVM for Forecasting Construction Output: A Case Study of Hubei Province |
| title_sort | application of gsm svm for forecasting construction output a case study of hubei province |
| topic | construction output support vector machine economic growth forecast model |
| url | https://www.mdpi.com/2075-5309/13/1/48 |
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