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Grain Security Risk Level Prediction Using ANFIS

Abstract---Food security is a major worldwide issue nowadays. One of the supporting indicators of the food security level is the trend of the global agriculture output per capita. In this study, grain data from China between 1997 and 2007 is used as a means to indicate the level of grain security. T...

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Main Authors: Muhd Khairulzaman Abdul Kadir, Evor L. Hines, Saharul Arof, Daciana Iliescu, Mark Leeson, Elizabeth Dowler, Rosemary Collier, Richard Napier, Qaddoum Kefaya, Reza Ghaffari, UniKL MSI
Format: Article
Language:English
Published: IEEE 2014
Subjects:
ANFIS
food security
grain security
risk level
Neural Network
Online Access:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6076340
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spelling my.unikl.ir-89822014-12-12T01:17:14Z Grain Security Risk Level Prediction Using ANFIS Muhd Khairulzaman Abdul Kadir, Evor L. Hines Saharul Arof, Daciana Iliescu Mark Leeson, Elizabeth Dowler Rosemary Collier, Richard Napier Qaddoum Kefaya, Reza Ghaffari, UniKL MSI ANFIS food security grain security risk level Neural Network Abstract---Food security is a major worldwide issue nowadays. One of the supporting indicators of the food security level is the trend of the global agriculture output per capita. In this study, grain data from China between 1997 and 2007 is used as a means to indicate the level of grain security. The inputs for this study are based on 3 categories; productive indexes, consumptive indexes, disaster indexes; in total there are 11 input indexes to the system with 2 membership functions (MFs) for each input. The system output is the level of the grain security, where the target data is based on a previous study of China grain security level. We use an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the grain security level. In this case data preprocessing with the Principal Component Analysis (PCA) technique was used to reduce inputs to 6 to avoid too many rule parameters which would affect the optimization performance of the model. A Multi-Layer Perceptron-Neural-Network (MLP-NN) model is used to compare with the performance of ANFIS. The result of this study shows that the resulting regression value in the case of ANFIS is around 0.99 which is better than that for the NN; which is around 0.60. Hence the ANFIS model is shown to offer better predictor of grain security level. It may also be an attractive method to explore further as a means for food security early warning monitoring systems. 2014-12-12T01:15:59Z 2014-12-12T01:15:59Z 2011-09 Article Kadir, M.K.A.; Hines, E.L.; Arof, S.; Illiescu, D.; Leeson, M.; Dowler, E.; Collier, R.; Napier, R.; Kefaya, Q.; Ghafari, R., "Grain Security Risk Level Prediction Using ANFIS," Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on , vol., no., pp.103,107, 20-22 Sept. 2011 doi: 10.1109/CIMSim.2011.27 978-1-4577-1797-0 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6076340 en IEEE
institution Universiti Kuala Lumpur
building UniKL Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kuala Lumpur
content_source UniKL Institutional Repository
url_provider http://ir.unikl.edu.my/
language English
topic ANFIS
food security
grain security
risk level
Neural Network
spellingShingle ANFIS
food security
grain security
risk level
Neural Network
Muhd Khairulzaman Abdul Kadir, Evor L. Hines
Saharul Arof, Daciana Iliescu
Mark Leeson, Elizabeth Dowler
Rosemary Collier, Richard Napier
Qaddoum Kefaya, Reza Ghaffari, UniKL MSI
Grain Security Risk Level Prediction Using ANFIS
description Abstract---Food security is a major worldwide issue nowadays. One of the supporting indicators of the food security level is the trend of the global agriculture output per capita. In this study, grain data from China between 1997 and 2007 is used as a means to indicate the level of grain security. The inputs for this study are based on 3 categories; productive indexes, consumptive indexes, disaster indexes; in total there are 11 input indexes to the system with 2 membership functions (MFs) for each input. The system output is the level of the grain security, where the target data is based on a previous study of China grain security level. We use an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the grain security level. In this case data preprocessing with the Principal Component Analysis (PCA) technique was used to reduce inputs to 6 to avoid too many rule parameters which would affect the optimization performance of the model. A Multi-Layer Perceptron-Neural-Network (MLP-NN) model is used to compare with the performance of ANFIS. The result of this study shows that the resulting regression value in the case of ANFIS is around 0.99 which is better than that for the NN; which is around 0.60. Hence the ANFIS model is shown to offer better predictor of grain security level. It may also be an attractive method to explore further as a means for food security early warning monitoring systems.
format Article
author Muhd Khairulzaman Abdul Kadir, Evor L. Hines
Saharul Arof, Daciana Iliescu
Mark Leeson, Elizabeth Dowler
Rosemary Collier, Richard Napier
Qaddoum Kefaya, Reza Ghaffari, UniKL MSI
author_facet Muhd Khairulzaman Abdul Kadir, Evor L. Hines
Saharul Arof, Daciana Iliescu
Mark Leeson, Elizabeth Dowler
Rosemary Collier, Richard Napier
Qaddoum Kefaya, Reza Ghaffari, UniKL MSI
author_sort Muhd Khairulzaman Abdul Kadir, Evor L. Hines
title Grain Security Risk Level Prediction Using ANFIS
title_short Grain Security Risk Level Prediction Using ANFIS
title_full Grain Security Risk Level Prediction Using ANFIS
title_fullStr Grain Security Risk Level Prediction Using ANFIS
title_full_unstemmed Grain Security Risk Level Prediction Using ANFIS
title_sort grain security risk level prediction using anfis
publisher IEEE
publishDate 2014
url http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6076340
_version_ 1644485029647089664
score 13.226497

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