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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2018
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| Subjects: | |
| 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 |
| Summary: | 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%. |
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