Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models
Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomato...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922000351 |
| _version_ | 1798029112919457792 |
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| author | Ommolbanin Bazrafshan Mohammad Ehteram Sarmad Dashti Latif Yuk Feng Huang Fang Yenn Teo Ali Najah Ahmed Ahmed El-Shafie |
| author_facet | Ommolbanin Bazrafshan Mohammad Ehteram Sarmad Dashti Latif Yuk Feng Huang Fang Yenn Teo Ali Najah Ahmed Ahmed El-Shafie |
| author_sort | Ommolbanin Bazrafshan |
| collection | DOAJ |
| description | Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this study, tomato yield based on data of 40 cities of Iran country including annual average temperature (T), relative humidity (RH), effective rainfall (R), wind speed (WS), and Evapotranspiration (EV) for the period of 1968–2018 was predicted using a new Bayesian model averaging (BMA). The paper's main innovation is the use of the new BMA so that it allows the modellers to quantify the uncertainty of model parameters and inputs simultaneously. For this aim, first, the multiple Adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) were used for predicting crop yield. To train the ANFIS and MLP model, a new algorithm, namely, multi verse optimization algorithm (MOA) was used. Also, the ability of MOA was benchmarked against the particle swarm optimization (PSO), and firefly algorithm (FFA). In the next level, the new BMA used the outputs of the ANFIS-MOA, MLP-MOA, ANFIS, FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP for predicting tomato yield in an ensemble framework. The five- input combination of RH, T, and R, WS, and EV gave the best result. The mean absolute error (MAE) of the BMA in the testing level was 20.12 (Ton/ha) while it was 24.12, 24.45, 24.67, 25.12, 29.12, 30.12, 31.12, and 33.45 for the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models. Regarding the results of uncertainty analysis, the uncertainty of BMA was lower than those of the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models while the MLP model provided the highest uncertainty. The results of this study indicated that BMA using multiple MLP and ANFIS model was useful for predicting tomato yield. |
| first_indexed | 2024-04-11T19:19:08Z |
| format | Article |
| id | doaj.art-85fad3519c774ab29e522f65aaad43dc |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-11T19:19:08Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-85fad3519c774ab29e522f65aaad43dc2022-12-22T04:07:21ZengElsevierAin Shams Engineering Journal2090-44792022-09-01135101724Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP modelsOmmolbanin Bazrafshan0Mohammad Ehteram1Sarmad Dashti Latif2Yuk Feng Huang3Fang Yenn Teo4Ali Najah Ahmed5Ahmed El-Shafie6Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources Engineering, University of Hormozgan. P.O. BOX: 3995, Bandar-Abbas, Iran; Corresponding authors.Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, IranCivil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaimany 46001, Kurdistan Region, IraqDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia; Corresponding authors.Faculty of Science and Engineering, University of Nottingham Malaysia, 43500 Semenyih, Selangor, MalaysiaDepartment of Civil Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Selangor, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia; National Water Center (NWC), United Arab Emirates University, Al Ain P.O. Box. 15551, UAEPredicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this study, tomato yield based on data of 40 cities of Iran country including annual average temperature (T), relative humidity (RH), effective rainfall (R), wind speed (WS), and Evapotranspiration (EV) for the period of 1968–2018 was predicted using a new Bayesian model averaging (BMA). The paper's main innovation is the use of the new BMA so that it allows the modellers to quantify the uncertainty of model parameters and inputs simultaneously. For this aim, first, the multiple Adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) were used for predicting crop yield. To train the ANFIS and MLP model, a new algorithm, namely, multi verse optimization algorithm (MOA) was used. Also, the ability of MOA was benchmarked against the particle swarm optimization (PSO), and firefly algorithm (FFA). In the next level, the new BMA used the outputs of the ANFIS-MOA, MLP-MOA, ANFIS, FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP for predicting tomato yield in an ensemble framework. The five- input combination of RH, T, and R, WS, and EV gave the best result. The mean absolute error (MAE) of the BMA in the testing level was 20.12 (Ton/ha) while it was 24.12, 24.45, 24.67, 25.12, 29.12, 30.12, 31.12, and 33.45 for the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models. Regarding the results of uncertainty analysis, the uncertainty of BMA was lower than those of the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models while the MLP model provided the highest uncertainty. The results of this study indicated that BMA using multiple MLP and ANFIS model was useful for predicting tomato yield.http://www.sciencedirect.com/science/article/pii/S2090447922000351Crop yieldAgricultureANFISMLPClimate parameters |
| spellingShingle | Ommolbanin Bazrafshan Mohammad Ehteram Sarmad Dashti Latif Yuk Feng Huang Fang Yenn Teo Ali Najah Ahmed Ahmed El-Shafie Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models Ain Shams Engineering Journal Crop yield Agriculture ANFIS MLP Climate parameters |
| title | Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models |
| title_full | Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models |
| title_fullStr | Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models |
| title_full_unstemmed | Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models |
| title_short | Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models |
| title_sort | predicting crop yields using a new robust bayesian averaging model based on multiple hybrid anfis and mlp models |
| topic | Crop yield Agriculture ANFIS MLP Climate parameters |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922000351 |
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