Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status
The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disas...
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| Language: | English |
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Institute of Advanced Engineering and Science (IAES)
2019
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| Online Access: | http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/1/43%201504%20102.%20Habaebi%20Flood%20IoT%20ed%20zly.pdf |
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iium-716202019-07-12T03:10:02Z http://irep.iium.edu.my/71620/ Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status Abdullahi, Salami Ifedapo Habaebi, Mohamed Hadi Abdul Malik, Noreha TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network is used. Institute of Advanced Engineering and Science (IAES) 2019-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/71620/1/43%201504%20102.%20Habaebi%20Flood%20IoT%20ed%20zly.pdf Abdullahi, Salami Ifedapo and Habaebi, Mohamed Hadi and Abdul Malik, Noreha (2019) Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status. Bulletin of Electrical Engineering and Informatics, 8 (2). pp. 708-719. ISSN 2089-3191 E-ISSN 2302-9285 http://journal.portalgaruda.org/index.php/EEI/article/view/1504 10.11591/eei.v8i2.1504 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Abdullahi, Salami Ifedapo Habaebi, Mohamed Hadi Abdul Malik, Noreha Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status |
| description |
The number of natural disasters occurring yearly is increasing at an alarming
rate which has caused a great concern over the well-being of human lives and
economy sustenance. The rainfall pattern has also been affected and this has
caused immense amount of flood cases in recent times. Flood disasters are
damaging to economy and human lives. Yearly, millions of people are
affected by floods in Asia alone. This has brought the attention of the
government to develop a flood forecasting method to reduce flood casualties.
In this article, a flood mitigation method will be evaluated which
incorporates a miniaturized flow, water level sensor and pressure gauge. The
data from the two sensors are used to predict flood status using a 2-class
neural network. Real-time monitoring of the data from the sensor into
Thingspeak channel were possible with the use of NodeMCU ESP8266.
Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in
2-class neural network which was used to predict flood status according to
predefine rule. The prediction model has been published as Web services
through AzureML service and it enables prediction as new data are available.
The experimental result showed that using 3 hidden layers has the highest
accuracy of 98.9% and precision of 100% when 2-class neural network
is used. |
| format |
Article |
| author |
Abdullahi, Salami Ifedapo Habaebi, Mohamed Hadi Abdul Malik, Noreha |
| author_facet |
Abdullahi, Salami Ifedapo Habaebi, Mohamed Hadi Abdul Malik, Noreha |
| author_sort |
Abdullahi, Salami Ifedapo |
| title |
Intelligent flood disaster warning on the fly: developing
IoT-based management platform and using 2-class neural
network to predict flood status |
| title_short |
Intelligent flood disaster warning on the fly: developing
IoT-based management platform and using 2-class neural
network to predict flood status |
| title_full |
Intelligent flood disaster warning on the fly: developing
IoT-based management platform and using 2-class neural
network to predict flood status |
| title_fullStr |
Intelligent flood disaster warning on the fly: developing
IoT-based management platform and using 2-class neural
network to predict flood status |
| title_full_unstemmed |
Intelligent flood disaster warning on the fly: developing
IoT-based management platform and using 2-class neural
network to predict flood status |
| title_sort |
intelligent flood disaster warning on the fly: developing
iot-based management platform and using 2-class neural
network to predict flood status |
| publisher |
Institute of Advanced Engineering and Science (IAES) |
| publishDate |
2019 |
| url |
http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/ http://irep.iium.edu.my/71620/1/43%201504%20102.%20Habaebi%20Flood%20IoT%20ed%20zly.pdf |
| first_indexed |
2023-09-18T21:41:33Z |
| last_indexed |
2023-09-18T21:41:33Z |
| _version_ |
1777413173359411200 |