Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria

A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process. Hidden Markov model is very influential in stochastic world because of its uniqueness, double stochast...

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Main Authors: A Lawal, U.Y. Abubakar, H Danladi, A.S. Gana
Format: Article
Language:English
Published: Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP) 2016-11-01
Series:Journal of Applied Sciences and Environmental Management
Subjects:
Online Access:https://www.ajol.info/index.php/jasem/article/view/147056
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author A Lawal
U.Y. Abubakar
H Danladi
A.S. Gana
author_facet A Lawal
U.Y. Abubakar
H Danladi
A.S. Gana
author_sort A Lawal
collection DOAJ
description A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process. Hidden Markov model is very influential in stochastic world because of its uniqueness, double stochastic nature and independence assumption between consecutive observations. A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual rainfall amount into three states, each with eight possible observations. The parameters of the model were estimated from the annual rainfall data of Jos, Plateau state, Nigeria for the period of 39 years (1977-2015). After which, the model was trained using Baum-Welch algorithm to attend maximum likelihood. The model is designed such that, if given any of the three rainfall states and its observation in the present year, it is possible to make quantitative prediction on how rainfall will be in the following year and in the subsequent years. The test HMM1 was able to make prediction with 75% accuracy in state and 50% accuracy in observations. The accuracy level of the model shows that, it is dependable and therefore, information from the model could be used as a guide to the farmers, agronomists, water resources management scientists and the government to plan strategies for crop production in the region. Keywords: Markov model, Hidden Markov model, Transition probability, Observation probability, Crop Production, Annual Rainfall
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spelling doaj.art-acdab47101bd4ef89a4ef1c5080a25352024-04-02T19:52:48ZengJoint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)Journal of Applied Sciences and Environmental Management2659-15022659-14992016-11-0120310.4314/jasem.v20i3.16Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, NigeriaA LawalU.Y. AbubakarH DanladiA.S. Gana A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process. Hidden Markov model is very influential in stochastic world because of its uniqueness, double stochastic nature and independence assumption between consecutive observations. A hidden Markov model to predict annual rainfall pattern has been presented in this paper. The model is developed to provide necessary information for the farmers, agronomists, water resource management scientists and policy makers to enable them plan for the uncertainty of annual rainfall. The model classified annual rainfall amount into three states, each with eight possible observations. The parameters of the model were estimated from the annual rainfall data of Jos, Plateau state, Nigeria for the period of 39 years (1977-2015). After which, the model was trained using Baum-Welch algorithm to attend maximum likelihood. The model is designed such that, if given any of the three rainfall states and its observation in the present year, it is possible to make quantitative prediction on how rainfall will be in the following year and in the subsequent years. The test HMM1 was able to make prediction with 75% accuracy in state and 50% accuracy in observations. The accuracy level of the model shows that, it is dependable and therefore, information from the model could be used as a guide to the farmers, agronomists, water resources management scientists and the government to plan strategies for crop production in the region. Keywords: Markov model, Hidden Markov model, Transition probability, Observation probability, Crop Production, Annual Rainfall https://www.ajol.info/index.php/jasem/article/view/147056Markov modelHidden Markov modelTransition probabilityObservation probabilityCrop ProductionAnnual Rainfall
spellingShingle A Lawal
U.Y. Abubakar
H Danladi
A.S. Gana
Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
Journal of Applied Sciences and Environmental Management
Markov model
Hidden Markov model
Transition probability
Observation probability
Crop Production
Annual Rainfall
title Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
title_full Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
title_fullStr Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
title_full_unstemmed Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
title_short Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria
title_sort prediction of annual rainfall pattern using hidden markov model hmm in jos plateau state nigeria
topic Markov model
Hidden Markov model
Transition probability
Observation probability
Crop Production
Annual Rainfall
url https://www.ajol.info/index.php/jasem/article/view/147056
work_keys_str_mv AT alawal predictionofannualrainfallpatternusinghiddenmarkovmodelhmminjosplateaustatenigeria
AT uyabubakar predictionofannualrainfallpatternusinghiddenmarkovmodelhmminjosplateaustatenigeria
AT hdanladi predictionofannualrainfallpatternusinghiddenmarkovmodelhmminjosplateaustatenigeria
AT asgana predictionofannualrainfallpatternusinghiddenmarkovmodelhmminjosplateaustatenigeria