Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses
Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/7/933 |
| _version_ | 1797407934581309440 |
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| author | Guillaume Grégoire Josée Fortin Isa Ebtehaj Hossein Bonakdari |
| author_facet | Guillaume Grégoire Josée Fortin Isa Ebtehaj Hossein Bonakdari |
| author_sort | Guillaume Grégoire |
| collection | DOAJ |
| description | Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In this study, we used a database of about 14,000 pesticide applications in the province of Québec, Canada, to develop a novel hybrid machine learning approach to predict pesticide use on golf courses. We created this proposed model, called RF-SVM-GOA, by coupling a support vector machine (SVM) with random forest (RF) and the grasshopper optimization algorithm (GOA). We applied RF to handle the wide range of datasets and GOA to find the optimal SVM settings. We considered five different dependent variables—region, golf course ID, number of holes, year, and treated area—as input variables. The experimental results confirmed that the developed hybrid RF-SVM-GOA approach was able to estimate the active ingredient total (AIT) with a high level of accuracy (R = 0.99; MAE = 0.84; RMSE = 0.84; NRMSE = 0.04). We compared the results produced by the developed RF-SVM-GOA model with those of four tree-based techniques including M5P, random tree, reduced error pruning tree (REP tree), and RF, as well as with those of two non-tree-based techniques including the generalized structure of group method of data handling (GSGMDH) and evolutionary polynomial regression (EPR). The computational results showed that the accuracy of the proposed RF-SVM-GOA approach was higher, outperforming the other methods. We analyzed sensitivity to find the most effective variables in AIT forecasting. The results indicated that the treated area is the most effective variable in AIT forecasting. The results of the current study provide a method for increasing the sustainability of golf course management. |
| first_indexed | 2024-03-09T03:49:57Z |
| format | Article |
| id | doaj.art-e5060e17d6224123a59632c0244ea726 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T03:49:57Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-e5060e17d6224123a59632c0244ea7262023-12-03T14:28:52ZengMDPI AGAgriculture2077-04722022-06-0112793310.3390/agriculture12070933Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf CoursesGuillaume Grégoire0Josée Fortin1Isa Ebtehaj2Hossein Bonakdari3Centre de Recherche et d’Innovation sur les Végétaux, Département de Phytologie, Université Laval, Québec, QC G1V 0A6, CanadaDepartment of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, CanadaDepartment of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, CanadaDepartment of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V 0A6, CanadaGolf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course managers reduce their reliance on these products. In this study, we used a database of about 14,000 pesticide applications in the province of Québec, Canada, to develop a novel hybrid machine learning approach to predict pesticide use on golf courses. We created this proposed model, called RF-SVM-GOA, by coupling a support vector machine (SVM) with random forest (RF) and the grasshopper optimization algorithm (GOA). We applied RF to handle the wide range of datasets and GOA to find the optimal SVM settings. We considered five different dependent variables—region, golf course ID, number of holes, year, and treated area—as input variables. The experimental results confirmed that the developed hybrid RF-SVM-GOA approach was able to estimate the active ingredient total (AIT) with a high level of accuracy (R = 0.99; MAE = 0.84; RMSE = 0.84; NRMSE = 0.04). We compared the results produced by the developed RF-SVM-GOA model with those of four tree-based techniques including M5P, random tree, reduced error pruning tree (REP tree), and RF, as well as with those of two non-tree-based techniques including the generalized structure of group method of data handling (GSGMDH) and evolutionary polynomial regression (EPR). The computational results showed that the accuracy of the proposed RF-SVM-GOA approach was higher, outperforming the other methods. We analyzed sensitivity to find the most effective variables in AIT forecasting. The results indicated that the treated area is the most effective variable in AIT forecasting. The results of the current study provide a method for increasing the sustainability of golf course management.https://www.mdpi.com/2077-0472/12/7/933active ingredients total (AIT)golf coursesgrasshopper optimization algorithm (GOA)hybrid modelrandom forest (RF)pesticides |
| spellingShingle | Guillaume Grégoire Josée Fortin Isa Ebtehaj Hossein Bonakdari Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses Agriculture active ingredients total (AIT) golf courses grasshopper optimization algorithm (GOA) hybrid model random forest (RF) pesticides |
| title | Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses |
| title_full | Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses |
| title_fullStr | Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses |
| title_full_unstemmed | Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses |
| title_short | Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses |
| title_sort | novel hybrid statistical learning framework coupled with random forest and grasshopper optimization algorithm to forecast pesticide use on golf courses |
| topic | active ingredients total (AIT) golf courses grasshopper optimization algorithm (GOA) hybrid model random forest (RF) pesticides |
| url | https://www.mdpi.com/2077-0472/12/7/933 |
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