Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network
Abstract
:1. Introduction
2. System Framework and Hardware Design
3. Forecasting Principles and Methods
3.1. K-Means++ Data Fusion
3.2. LSTM Neural Network
3.3. Combination of K-Means++ Algorithm and LSTM Neural Network
3.4. Model Evaluation Indicators
4. Results and Discussion
4.1. Experimental Preparation
4.2. Forecast Results
4.2.1. K-Means++ Data Fusion Results
4.2.2. LSTM Neural Network Prediction Results
4.2.3. Evaluation of Forecast Results
4.2.4. Comparison
5. Practical Applications
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Node | Time | Temperature/°C | Relative Humidity/% |
---|---|---|---|
1 | 07:05:04 | −20.2 | 79.3 |
4 | 07:15:02 | −20.3 | 79.2 |
2 | 07:25:56 | −20 | 79.8 |
3 | 07:35:23 | −20.2 | 78.8 |
2 | 07:45:12 | −20.1 | 70.8 |
… | … | … | … |
4 | 15:22:28 | −10 | 67.84 |
2 | 15:32:22 | −19.3 | 70.01 |
3 | 15:42:34 | −19.6 | 72.21 |
1 | 15:52:01 | −19.9 | 74.41 |
2 | 15:02:46 | −20.2 | 75.01 |
Node | Number of Nodes in the Hidden Layer | Time Steps |
---|---|---|
1 | 5 | 5 |
2 | 25 | 1 |
3 | 35 | 2 |
4 | 45 | 3 |
Node | Number of Nodes in the Hidden Layer | Time Steps |
---|---|---|
1 | 5 | 3 |
2 | 5 | 1 |
3 | 10 | 1 |
4 | 15 | 2 |
Node | Temperature/°C | Relative Humidity/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | MSE | MAPE | R-Squared | RMSE | MAE | MSE | MAPE | R-Squared | |
1 | 0.5707 | 0.4284 | 0.3258 | 0.0312 | 0.9724 | 1.6015 | 1.1770 | 2.5648 | 0.2736 | 0.9702 |
2 | 0.5770 | 0.4118 | 0.3329 | 0.0356 | 0.9652 | 2.4152 | 1.3956 | 5.8333 | 0.6237 | 0.9322 |
3 | 0.6189 | 0.4896 | 0.3831 | 0.0408 | 0.9600 | 2.4035 | 1.3885 | 5.7772 | 0.5886 | 0.9329 |
4 | 0.5826 | 0.4431 | 0.3394 | 0.0348 | 0.9646 | 1.7535 | 1.0510 | 3.0748 | 0.3547 | 0.9643 |
Average value | 0.5873 | 0.4432 | 0.3453 | 0.0356 | 0.9655 | 2.0434 | 1.2530 | 4.3125 | 1.8406 | 0.9499 |
Model | Temperature/°C | Relative Humidity/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | MSE | MAPE | R-Squared | RMSE | MAE | MSE | MAPE | R-Squared | |
k-means++–BP | 0.7681 | 0.5943 | 0.5901 | 0.0634 | 0.9384 | 2.3917 | 1.5816 | 5.7206 | 0.5543 | 0.9335 |
k-means++–LSTM | 0.5707 | 0.4284 | 0.3258 | 0.0312 | 0.9660 | 1.6015 | 1.1770 | 2.5648 | 0.2736 | 0.9702 |
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Jiang, J.; Peng, C.; Liu, W.; Liu, S.; Luo, Z.; Chen, N. Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network. Processes 2023, 11, 776. https://doi.org/10.3390/pr11030776
Jiang J, Peng C, Liu W, Liu S, Luo Z, Chen N. Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network. Processes. 2023; 11(3):776. https://doi.org/10.3390/pr11030776
Chicago/Turabian StyleJiang, Junjie, Cuiling Peng, Wenjing Liu, Shuangyin Liu, Zhijie Luo, and Ningxia Chen. 2023. "Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network" Processes 11, no. 3: 776. https://doi.org/10.3390/pr11030776
APA StyleJiang, J., Peng, C., Liu, W., Liu, S., Luo, Z., & Chen, N. (2023). Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network. Processes, 11(3), 776. https://doi.org/10.3390/pr11030776