Name
Title | ||
|---|---|---|
An automatic model configuration and optimization system for milk production forecasting. |
Abstract | ||
|---|---|---|
The Milk Production Forecast Optimization System (MPFOS) was presented.An Adaptive Stratified Sampling Approach was applied.Nine milk production forecasting models were configured and compared.The optimal milk production forecasting model was selected using the MPFOS output. The objective of this study was to develop and implement the Milk Production Forecast Optimization System (MPFOS) for the purpose of comparing the effectiveness of multiple herd milk yield prediction models for an Irish pasture-based dairy herd. The MPFOS was populated by nine milk production models that were categorized into three types: curve fitting, regression and auto-regressive models. The Adaptive Stratified Sampling Approach (ASSA) was introduced for data filtering, processing and for randomly selecting each member of the 100 cow sample herd. The MPFOS calculated optimal model parameters, statistical analysis and milk production forecasts for each chosen model using input data combinations based on animal, herd and milk production records. The model evaluations were based on historical milk production data between the years 2004 and 2009 from dairy farms in the south of Ireland situated in close proximity. Milk yield records from 2004 to 2008 were used for model training, whereas the milk production records for 2009 were set for model evaluation and validation. The ASSA randomly selected the representative herd population based on the required criteria. The MPFOS automatically generated the optimal configuration for each of the nine milk production forecast models and benchmarked their performance over a short, medium and long term prediction horizon. The Root Mean Square Error (RMSE) value of the nine prediction models varied substantially (from 68.5kg to 210.4kg per day). The surface fitting model performed better (10% in RPE and R2) than the dynamic NARX model for the same prediction horizon (365-day and 30-day). However, the NARX model provided more accurate results for shorter (10-day) prediction horizons. The MPFOS found the most accurate model based on prediction horizon length and on number of input parameters. The results of this study demonstrate the effectiveness of the MPFOS as a model configuration and comparison tool. The MPFOS may also be employed for selecting the optimal milk production forecast model for a specific application. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.compag.2016.08.016 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
MPFOS,ASSA,DIM,NCM,DHMY,SANN,ANN,MLR,NARX,SSE,R-square,RMSE,RPE,GUI | Econometrics,Population,Nonlinear autoregressive exogenous model,Regression,Long-term prediction,Curve fitting,Mean squared error,Engineering,Predictive modelling,Coefficient of determination | Journal |
Volume | Issue | ISSN |
128 | C | 0168-1699 |
Citations | PageRank | References |
4 | 0.59 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhang | 1 | 229 | 69.82 |
| Michael D. Murphy | 2 | 4 | 2.28 |
| Laurence Shalloo | 3 | 11 | 1.72 |
| Elodie Ruelle | 4 | 4 | 1.61 |
| John Upton | 5 | 4 | 1.94 |