How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.1.1. Measurement Site
2.1.2. Measurement Setup
2.1.3. Derivation of Hourly Emission Values
2.2. Regression Analysis
2.2.1. Machine Learning Methods
2.2.2. Sampling of Training Data and Cross-Validation
2.2.3. Evaluation Measures
2.2.4. Graphical Representations
3. Results
3.1. Prediction Accuracy
3.2. Evaluation Criterion and Model Selection
3.3. Scenarios of Temporal Sampling
4. Discussion
4.1. Importance of Temporal Sampling and Added Value of Machine Learning
4.2. Sound Selection of Model Evaluation Criteria
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
FTIR | Fourier Transform Infrared |
LU | Livestock Unit (500 g body mass equivalent) |
GradBoost | Gradient Boosting |
RandForest | Random Forest |
Ridge | Regularized Multilinear Regression |
LinReg | Ordinary Multilinear Regression |
ANN | Artificial Neural Network |
fixAnn | Artificial Neural Network with fixed number of hidden layers and nodes |
GaussProc | Gaussian Process |
SVM | Support Vector Machine |
MAE | Mean Absolute Error |
RMSE | Root Mean Square Error |
TAE | Total Absolute Error |
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Index | |||||
---|---|---|---|---|---|
1, 2, 3 | 1, 7, 14 | 3 | 1 | 1 | 1 |
4, 5, 6 | 1, 7, 14 | 4 | 2 | 1 | 1 |
7, 8, 9 | 1, 7, 14 | 4 | 2 | 2 | 0 |
10, 11, 12 | 1, 7, 14 | 4 | 3 | 1 | 0 |
13, 14, 15 | 1, 7, 14 | 6 | 2 | 2 | 2 |
16, 17, 18 | 1, 7, 14 | 6 | 3 | 2 | 1 |
19, 20, 21 | 1, 7, 14 | 6 | 4 | 1 | 1 |
22, 23, 24 | 1, 7, 14 | 6 | 4 | 2 | 0 |
25, 26, 27 | 1, 7, 14 | 6 | 5 | 1 | 0 |
Scenario Unit | MAE ln [g h−1 LU−1] | RMSE 1 | R2 | TAE g h−1 LU−1 | TAE % |
---|---|---|---|---|---|
1 | 0.441 | 0.350 | 0.236 | 0.0782 | 5.6 |
2 | 0.428 | 0.333 | 0.260 | 0.0830 | 5.9 |
3 | 0.409 | 0.314 | 0.297 | 0.0519 | 3.7 |
4 | 0.413 | 0.314 | 0.318 | 0.0747 | 5.3 |
5 | 0.404 | 0.303 | 0.335 | 0.0780 | 5.6 |
6 | 0.391 | 0.291 | 0.359 | 0.0437 | 3.1 |
7 | 0.480 | 0.418 | 0.088 | 0.0413 | 2.9 |
8 | 0.449 | 0.373 | 0.182 | 0.0716 | 5.1 |
9 | 0.473 | 0.400 | 0.127 | 0.0456 | 3.2 |
10 | 0.459 | 0.376 | 0.180 | 0.0277 | 2.0 |
11 | 0.428 | 0.343 | 0.256 | 0.0258 | 1.8 |
12 | 0.399 | 0.304 | 0.346 | 0.0232 | 1.7 |
13 | 0.427 | 0.324 | 0.292 | 0.1461 | 10.4 |
14 | 0.399 | 0.298 | 0.341 | 0.0899 | 6.4 |
15 | 0.376 | 0.271 | 0.398 | 0.0537 | 3.8 |
16 | 0.423 | 0.330 | 0.280 | 0.0651 | 4.6 |
17 | 0.394 | 0.296 | 0.346 | 0.0609 | 4.3 |
18 | 0.401 | 0.299 | 0.332 | 0.0626 | 4.5 |
19 | 0.416 | 0.316 | 0.312 | 0.0998 | 7.1 |
20 | 0.395 | 0.292 | 0.363 | 0.0759 | 5.4 |
21 | 0.378 | 0.270 | 0.402 | 0.0586 | 4.2 |
22 | 0.426 | 0.332 | 0.276 | 0.0047 | 0.3 |
23 | 0.418 | 0.323 | 0.302 | 0.0133 | 1.0 |
24 | 0.407 | 0.309 | 0.336 | 0.0125 | 0.9 |
25 | 0.425 | 0.334 | 0.272 | 0.0253 | 1.8 |
26 | 0.399 | 0.296 | 0.352 | 0.0582 | 4.1 |
27 | 0.392 | 0.287 | 0.388 | 0.0472 | 3.4 |
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Hempel, S.; Adolphs, J.; Landwehr, N.; Janke, D.; Amon, T. How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning. Sustainability 2020, 12, 1030. https://doi.org/10.3390/su12031030
Hempel S, Adolphs J, Landwehr N, Janke D, Amon T. How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning. Sustainability. 2020; 12(3):1030. https://doi.org/10.3390/su12031030
Chicago/Turabian StyleHempel, Sabrina, Julian Adolphs, Niels Landwehr, David Janke, and Thomas Amon. 2020. "How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning" Sustainability 12, no. 3: 1030. https://doi.org/10.3390/su12031030