Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proporti...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Aswad Firas Mohammed, Kareem Ali Noori, Khudhur Ahmed Mahmood, Khalaf Bashar Ahmed, Mostafa Salama A.
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	flood prediction internet of things multivariate classification machine learning explainable artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e1040d7beee24ee281d8e167c156f98f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e1040d7beee24ee281d8e167c156f98f
record_format	dspace
spelling	oai:doaj.org-article:e1040d7beee24ee281d8e167c156f98f2021-12-05T14:10:51ZTree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction2191-026X10.1515/jisys-2021-0179https://doaj.org/article/e1040d7beee24ee281d8e167c156f98f2021-11-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0179https://doaj.org/toc/2191-026XFloods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system.Aswad Firas MohammedKareem Ali NooriKhudhur Ahmed MahmoodKhalaf Bashar AhmedMostafa Salama A.De Gruyterarticleflood predictioninternet of thingsmultivariate classificationmachine learningexplainable artificial intelligenceScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 31, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	flood prediction internet of things multivariate classification machine learning explainable artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	flood prediction internet of things multivariate classification machine learning explainable artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95 Aswad Firas Mohammed Kareem Ali Noori Khudhur Ahmed Mahmood Khalaf Bashar Ahmed Mostafa Salama A. Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
description	Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system.
format	article
author	Aswad Firas Mohammed Kareem Ali Noori Khudhur Ahmed Mahmood Khalaf Bashar Ahmed Mostafa Salama A.
author_facet	Aswad Firas Mohammed Kareem Ali Noori Khudhur Ahmed Mahmood Khalaf Bashar Ahmed Mostafa Salama A.
author_sort	Aswad Firas Mohammed
title	Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
title_short	Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
title_full	Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
title_fullStr	Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
title_full_unstemmed	Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
title_sort	tree-based machine learning algorithms in the internet of things environment for multivariate flood status prediction
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/e1040d7beee24ee281d8e167c156f98f
work_keys_str_mv	AT aswadfirasmohammed treebasedmachinelearningalgorithmsintheinternetofthingsenvironmentformultivariatefloodstatusprediction AT kareemalinoori treebasedmachinelearningalgorithmsintheinternetofthingsenvironmentformultivariatefloodstatusprediction AT khudhurahmedmahmood treebasedmachinelearningalgorithmsintheinternetofthingsenvironmentformultivariatefloodstatusprediction AT khalafbasharahmed treebasedmachinelearningalgorithmsintheinternetofthingsenvironmentformultivariatefloodstatusprediction AT mostafasalamaa treebasedmachinelearningalgorithmsintheinternetofthingsenvironmentformultivariatefloodstatusprediction
_version_	1718371694182137856

Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction

Ejemplares similares