Flood disaster warning system on the go
Floods are one of the top natural disaster that affects many regions around the world, harming human lives and lessening economy growth. Therefore, it is crucial to build an early warning system that forecast flow rate and water level to reduce the casualties of flood disaster. The objective of...
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| Format: | Conference or Workshop Item |
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IEEE
2018
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| Online Access: | http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/7/67951%20Flood%20Disaster%20Warning%20System%20on%20the%20go.pdf http://irep.iium.edu.my/67951/13/67951_Flood%20disaster%20warning%20system%20on%20the%20go_Scopus.pdf |
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iium-679512019-08-17T04:33:31Z http://irep.iium.edu.my/67951/ Flood disaster warning system on the go Abdullahi, Salami Ifedabo Habaebi, Mohamed Hadi Abdul Malik, Noreha TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Floods are one of the top natural disaster that affects many regions around the world, harming human lives and lessening economy growth. Therefore, it is crucial to build an early warning system that forecast flow rate and water level to reduce the casualties of flood disaster. The objective of this paper is to design a flood monitoring system which integrates both flow and water level sensor and use two class neural network to predict the flood status from stored data in the database. A laboratory experiment was carried out to simulate the system and a pressure gauge was utilized to measure the pressure of inflowing water. A NodeMCU ESP8266 enables transmission of sensor data to Thingspeak channel for real-time visualization and storing the data in database. Furthermore, two class neural network module built in Microsoft’s Azure Machine Learning (AzureML) was used to predict flood status according to a pre-define rule. The result of the 2-class neural network showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100%. IEEE 2018-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/67951/7/67951%20Flood%20Disaster%20Warning%20System%20on%20the%20go.pdf application/pdf en http://irep.iium.edu.my/67951/13/67951_Flood%20disaster%20warning%20system%20on%20the%20go_Scopus.pdf Abdullahi, Salami Ifedabo and Habaebi, Mohamed Hadi and Abdul Malik, Noreha (2018) Flood disaster warning system on the go. In: 2018 7th International Conference on Computer Communication Engineering (ICCCE2018), 19th-20th September 2018, Kuala Lumpur. https://ieeexplore.ieee.org/document/8539253 10.1109/ICCCE.2018.8539253 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Abdullahi, Salami Ifedabo Habaebi, Mohamed Hadi Abdul Malik, Noreha Flood disaster warning system on the go |
| description |
Floods are one of the top natural disaster that
affects many regions around the world, harming human lives
and lessening economy growth. Therefore, it is crucial to build
an early warning system that forecast flow rate and water level
to reduce the casualties of flood disaster. The objective of this
paper is to design a flood monitoring system which integrates
both flow and water level sensor and use two class neural
network to predict the flood status from stored data in the
database. A laboratory experiment was carried out to simulate
the system and a pressure gauge was utilized to measure the
pressure of inflowing water. A NodeMCU ESP8266 enables
transmission of sensor data to Thingspeak channel for real-time
visualization and storing the data in database. Furthermore, two
class neural network module built in Microsoft’s Azure
Machine Learning (AzureML) was used to predict flood status
according to a pre-define rule. The result of the 2-class neural
network showed that using 3 hidden layers has the highest
accuracy of 98.9% and precision of 100%. |
| format |
Conference or Workshop Item |
| author |
Abdullahi, Salami Ifedabo Habaebi, Mohamed Hadi Abdul Malik, Noreha |
| author_facet |
Abdullahi, Salami Ifedabo Habaebi, Mohamed Hadi Abdul Malik, Noreha |
| author_sort |
Abdullahi, Salami Ifedabo |
| title |
Flood disaster warning system on the go |
| title_short |
Flood disaster warning system on the go |
| title_full |
Flood disaster warning system on the go |
| title_fullStr |
Flood disaster warning system on the go |
| title_full_unstemmed |
Flood disaster warning system on the go |
| title_sort |
flood disaster warning system on the go |
| publisher |
IEEE |
| publishDate |
2018 |
| url |
http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/ http://irep.iium.edu.my/67951/7/67951%20Flood%20Disaster%20Warning%20System%20on%20the%20go.pdf http://irep.iium.edu.my/67951/13/67951_Flood%20disaster%20warning%20system%20on%20the%20go_Scopus.pdf |
| first_indexed |
2023-09-18T21:36:28Z |
| last_indexed |
2023-09-18T21:36:28Z |
| _version_ |
1777412854059630592 |