Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example
Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural pro...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Smart Agriculture
2023-03-01
|
| Series: | 智慧农业 |
| Subjects: | |
| Online Access: | http://www.smartag.net.cn/CN/10.12133/j.smartag.SA202302001 |
| _version_ | 1797793758336516096 |
|---|---|
| author | ZUO Min HU Tianyu DONG Wei ZHANG Kexin ZHANG Qingchuan |
| author_facet | ZUO Min HU Tianyu DONG Wei ZHANG Kexin ZHANG Qingchuan |
| author_sort | ZUO Min |
| collection | DOAJ |
| description | Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural products logistics demand is actually complicated, and it will be affected by various factors in the forecasting process. Therefore, in order to ensure the accuracy of forecasting the logistics demand of agricultural products, many influencing factors need to be considered. In this study, the logistics demand of agricultural products is taken as the research object, relevant indicators from 2017 to 2021 were selected as characteristic independent variables and a neural network model for forecasting the logistics demand of agricultural products was constructed by using Informer neural network. Taking Henan province, Hubei province and Hunan province in Central China as examples, the logistics demands of agricultural products in the three provinces were predicted. At the same time, long short-term memory network (LSTM) and Transformer neural network were used to forecast the demand of agricultural products logistics in three provinces of Central China, and the prediction results of the three models were compared. The results showed that the average percentage of prediction test error based on Informer neural network model constructed in this study was 3.39%, which was lower than that of LSTM and Transformer neural network models of 4.43% and 4.35%. The predicted value of Informer neural network model for three provinces was close to the actual value. The predicted value of Henan province in 2021 was 4185.33, the actual value was 4048.10, and the error was 3.389%. The predicted value of Hubei province in 2021 was 2503.64, the actual value was 2421.78, and the error was 3.380%. The predicted value of Hunan province in 2021 was 2933.31, the actual value was 2836.86, and the error was 3.340%. Therefore, it showed that the model can accurately predict the demand of agricultural products logistics in three provinces of Central China, and can provide a basis for rational planning and policy making of agricultural products logistics. Finally, the model and parameters were used to predict the logistics demand of agricultural products in Henan, Hunan, and Hubei provinces in 2023, and the predicted value of Henan province in 2023 was 4217.13; Hubei province was 2521.47, and Hunan province was 2974.65, respectively. The predicted values for the three provinces in 2023 are higher than the predicted values in 2021. Therefore, based on the logistics and transportation supporting facilities in 2021, it is necessary to ensure logistics and transportation efficiency and strengthen logistics and transportation capacity, so as to meet the growing logistics demand in Central China. |
| first_indexed | 2024-03-13T02:51:52Z |
| format | Article |
| id | doaj.art-cfc6474a83574c2fa8a273cc237c0881 |
| institution | Directory Open Access Journal |
| issn | 2096-8094 |
| language | English |
| last_indexed | 2024-03-13T02:51:52Z |
| publishDate | 2023-03-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj.art-cfc6474a83574c2fa8a273cc237c08812023-06-28T10:27:20ZengEditorial Office of Smart Agriculture智慧农业2096-80942023-03-0151344310.12133/j.smartag.SA202302001SA202302001Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An ExampleZUO MinHU TianyuDONG WeiZHANG KexinZHANG QingchuanEnsuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural products logistics demand is actually complicated, and it will be affected by various factors in the forecasting process. Therefore, in order to ensure the accuracy of forecasting the logistics demand of agricultural products, many influencing factors need to be considered. In this study, the logistics demand of agricultural products is taken as the research object, relevant indicators from 2017 to 2021 were selected as characteristic independent variables and a neural network model for forecasting the logistics demand of agricultural products was constructed by using Informer neural network. Taking Henan province, Hubei province and Hunan province in Central China as examples, the logistics demands of agricultural products in the three provinces were predicted. At the same time, long short-term memory network (LSTM) and Transformer neural network were used to forecast the demand of agricultural products logistics in three provinces of Central China, and the prediction results of the three models were compared. The results showed that the average percentage of prediction test error based on Informer neural network model constructed in this study was 3.39%, which was lower than that of LSTM and Transformer neural network models of 4.43% and 4.35%. The predicted value of Informer neural network model for three provinces was close to the actual value. The predicted value of Henan province in 2021 was 4185.33, the actual value was 4048.10, and the error was 3.389%. The predicted value of Hubei province in 2021 was 2503.64, the actual value was 2421.78, and the error was 3.380%. The predicted value of Hunan province in 2021 was 2933.31, the actual value was 2836.86, and the error was 3.340%. Therefore, it showed that the model can accurately predict the demand of agricultural products logistics in three provinces of Central China, and can provide a basis for rational planning and policy making of agricultural products logistics. Finally, the model and parameters were used to predict the logistics demand of agricultural products in Henan, Hunan, and Hubei provinces in 2023, and the predicted value of Henan province in 2023 was 4217.13; Hubei province was 2521.47, and Hunan province was 2974.65, respectively. The predicted values for the three provinces in 2023 are higher than the predicted values in 2021. Therefore, based on the logistics and transportation supporting facilities in 2021, it is necessary to ensure logistics and transportation efficiency and strengthen logistics and transportation capacity, so as to meet the growing logistics demand in Central China.http://www.smartag.net.cn/CN/10.12133/j.smartag.SA202302001agricultural productsdemand forecastinformer neural networklong short-term memory network (lstm)transformerdeep learning |
| spellingShingle | ZUO Min HU Tianyu DONG Wei ZHANG Kexin ZHANG Qingchuan Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example 智慧农业 agricultural products demand forecast informer neural network long short-term memory network (lstm) transformer deep learning |
| title | Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example |
| title_full | Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example |
| title_fullStr | Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example |
| title_full_unstemmed | Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example |
| title_short | Forecast and Analysis of Agricultural Products Logistics Demand Based on Informer Neural Network: Take the Central China Aera as An Example |
| title_sort | forecast and analysis of agricultural products logistics demand based on informer neural network take the central china aera as an example |
| topic | agricultural products demand forecast informer neural network long short-term memory network (lstm) transformer deep learning |
| url | http://www.smartag.net.cn/CN/10.12133/j.smartag.SA202302001 |
| work_keys_str_mv | AT zuomin forecastandanalysisofagriculturalproductslogisticsdemandbasedoninformerneuralnetworktakethecentralchinaaeraasanexample AT hutianyu forecastandanalysisofagriculturalproductslogisticsdemandbasedoninformerneuralnetworktakethecentralchinaaeraasanexample AT dongwei forecastandanalysisofagriculturalproductslogisticsdemandbasedoninformerneuralnetworktakethecentralchinaaeraasanexample AT zhangkexin forecastandanalysisofagriculturalproductslogisticsdemandbasedoninformerneuralnetworktakethecentralchinaaeraasanexample AT zhangqingchuan forecastandanalysisofagriculturalproductslogisticsdemandbasedoninformerneuralnetworktakethecentralchinaaeraasanexample |