Staff View: Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Bazrafshan, Ommolbanin, Ehteram, Mohammad, Latif, Sarmad Dashti, Huang, Yuk Feng, Teo, Fang Yenn, Ahmed, Ali Najah, Ahmed El-Shafie, Ahmed Hussein Kamel
Format:	Article
Published:	Elsevier 2022
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/42355/
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.um.eprints.42355
record_format	eprints
spelling	my.um.eprints.423552023-10-13T08:45:20Z http://eprints.um.edu.my/42355/ Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models Bazrafshan, Ommolbanin Ehteram, Mohammad Latif, Sarmad Dashti Huang, Yuk Feng Teo, Fang Yenn Ahmed, Ali Najah Ahmed El-Shafie, Ahmed Hussein Kamel TA Engineering (General). Civil engineering (General) 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. CO 2022 THE AUTHORS. Published by Elsevier BV Elsevier 2022-09 Article PeerReviewed Bazrafshan, Ommolbanin and Ehteram, Mohammad and Latif, Sarmad Dashti and Huang, Yuk Feng and Teo, Fang Yenn and Ahmed, Ali Najah and Ahmed El-Shafie, Ahmed Hussein Kamel (2022) Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models. Ain Shams Engineering Journal, 13 (5). ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2022.101724 <https://doi.org/10.1016/j.asej.2022.101724>. 10.1016/j.asej.2022.101724
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Bazrafshan, Ommolbanin Ehteram, Mohammad Latif, Sarmad Dashti Huang, Yuk Feng Teo, Fang Yenn Ahmed, Ali Najah Ahmed El-Shafie, Ahmed Hussein Kamel Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models
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. CO 2022 THE AUTHORS. Published by Elsevier BV
format	Article
author	Bazrafshan, Ommolbanin Ehteram, Mohammad Latif, Sarmad Dashti Huang, Yuk Feng Teo, Fang Yenn Ahmed, Ali Najah Ahmed El-Shafie, Ahmed Hussein Kamel
author_facet	Bazrafshan, Ommolbanin Ehteram, Mohammad Latif, Sarmad Dashti Huang, Yuk Feng Teo, Fang Yenn Ahmed, Ali Najah Ahmed El-Shafie, Ahmed Hussein Kamel
author_sort	Bazrafshan, Ommolbanin
title	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_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_sort	predicting crop yields using a new robust bayesian averaging model based on multiple hybrid anfis and mlp models
publisher	Elsevier
publishDate	2022
url	http://eprints.um.edu.my/42355/
_version_	1781704632194564096
score	13.211869

Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

Similar Items