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Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction

Urban flooding causing annual deaths and financial losses. Various technique has been applied to estimate the flood level. One of the low-cost solutions, which is using an initial sensor in the smartphone, can be used to predict the flood level, and respond to the warning message to the community by...

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Main Authors: Wan Bejuri, Wan Mohd Ya'akob, Mohamad, Abdul Karim
Format: Article
Language:English
Published: Academic Inspired Network 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26203/2/JISED-2022-46-07-16.PDF
http://eprints.utem.edu.my/id/eprint/26203/
http://www.jised.com/PDF/JISED-2022-46-07-16.pdf
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spelling my.utem.eprints.262032023-02-23T11:42:25Z http://eprints.utem.edu.my/id/eprint/26203/ Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction Wan Bejuri, Wan Mohd Ya'akob Mohamad, Abdul Karim Urban flooding causing annual deaths and financial losses. Various technique has been applied to estimate the flood level. One of the low-cost solutions, which is using an initial sensor in the smartphone, can be used to predict the flood level, and respond to the warning message to the community by sending it to the cloud. However, in the challenging situation during flood especially low internet connectivity, the processed particle data might be suffered from the sample impoverishment phenomenon, caused by sensor sensitivity and real-time transmission, which is lead to inaccuracy of flood level prediction. In this paper, a new particle filter will be designed and implemented in the mobile flood level prediction system by adapting noise and sample size from particle data input. This is for optimizing the data input, thus, can provide better flood level prediction and finally can share the alert information with other users. To accomplish this, numerous volunteers will take mobile sensor readings as more than 10000 readings in different depth levels, then, can be used to train machine learning models. Simulation results show that proposed particle filter achieved better performance compared with other variant when applied the flood level prediction system. In conclusion, the adaptive proposed. Academic Inspired Network 2022-07 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26203/2/JISED-2022-46-07-16.PDF Wan Bejuri, Wan Mohd Ya'akob and Mohamad, Abdul Karim (2022) Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction. Journal of Islamic, Social, Economics and Development (JISED), 7 (46). pp. 136-141. ISSN 0128-1755 http://www.jised.com/PDF/JISED-2022-46-07-16.pdf 10.55573/JISED.074615
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Urban flooding causing annual deaths and financial losses. Various technique has been applied to estimate the flood level. One of the low-cost solutions, which is using an initial sensor in the smartphone, can be used to predict the flood level, and respond to the warning message to the community by sending it to the cloud. However, in the challenging situation during flood especially low internet connectivity, the processed particle data might be suffered from the sample impoverishment phenomenon, caused by sensor sensitivity and real-time transmission, which is lead to inaccuracy of flood level prediction. In this paper, a new particle filter will be designed and implemented in the mobile flood level prediction system by adapting noise and sample size from particle data input. This is for optimizing the data input, thus, can provide better flood level prediction and finally can share the alert information with other users. To accomplish this, numerous volunteers will take mobile sensor readings as more than 10000 readings in different depth levels, then, can be used to train machine learning models. Simulation results show that proposed particle filter achieved better performance compared with other variant when applied the flood level prediction system. In conclusion, the adaptive proposed.
format Article
author Wan Bejuri, Wan Mohd Ya'akob
Mohamad, Abdul Karim
spellingShingle Wan Bejuri, Wan Mohd Ya'akob
Mohamad, Abdul Karim
Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
author_facet Wan Bejuri, Wan Mohd Ya'akob
Mohamad, Abdul Karim
author_sort Wan Bejuri, Wan Mohd Ya'akob
title Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
title_short Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
title_full Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
title_fullStr Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
title_full_unstemmed Adaptive Particle Filter (APF) by using noise and sample size for mobile flood level prediction
title_sort adaptive particle filter (apf) by using noise and sample size for mobile flood level prediction
publisher Academic Inspired Network
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26203/2/JISED-2022-46-07-16.PDF
http://eprints.utem.edu.my/id/eprint/26203/
http://www.jised.com/PDF/JISED-2022-46-07-16.pdf
_version_ 1758953849637306368
score 13.211869

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