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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2019
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| Subjects: | |
| 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 |
| Summary: | 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. |
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