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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Salami Ifedapo Abdullahi
International Islamic University Malaysia (IIUM)
Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abd Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.957 KB) | DOI: 10.11591/eei.v8i2.1504

Abstract

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.
Capacitive electrode sensor implanted on a printed circuit board designed for continuous water level measurement Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abdul Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.756 KB) | DOI: 10.11591/eei.v8i2.1515

Abstract

Water level sensors are one of the practical ways to get the actual measurement of the depth of a dam or canal. The ease of deployment and easy data acquisition makes them widely used in many fields. Therefore, it will be advantageous to have a miniaturized water level sensor for easier mobility and deployment. A novel method for measuring water level using a Printed Circuit Board has been proposed in this paper. The design stages of circuit sketching, printing of sketch on PCB and etching are discussed for the electrode water level sensor. A signal conditioning circuit is necessary to maintain a steady flow of current from the power source. The fabricated electrode water level sensor was tested based on its capacitive effect while charging up and the amount of current at each electrode finger at the saturation stage. The hardware enablers for this test were the multimeter and LCR meter. Arduino microprocessor was used to test and measure the transient response time for each electrode finger. The transient response sensitivity of the electrode sensor is measured to be 0.0873 millisecond/cm while the resolution of the electrode sensor is 0.1cm over a range of 30cm water level. A multiple correlation of 0.921 was achieved for the water level, measured current and measured capacitance with P-values less than 0.05 indicating strength of the data obtained from the tests conducted. The result showed strong evidence that the electrode water level sensor can be an alternative method of measuring water level.
Capacitive electrode sensor implanted on a printed circuit board designed for continuous water level measurement Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abdul Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.756 KB) | DOI: 10.11591/eei.v8i2.1515

Abstract

Water level sensors are one of the practical ways to get the actual measurement of the depth of a dam or canal. The ease of deployment and easy data acquisition makes them widely used in many fields. Therefore, it will be advantageous to have a miniaturized water level sensor for easier mobility and deployment. A novel method for measuring water level using a Printed Circuit Board has been proposed in this paper. The design stages of circuit sketching, printing of sketch on PCB and etching are discussed for the electrode water level sensor. A signal conditioning circuit is necessary to maintain a steady flow of current from the power source. The fabricated electrode water level sensor was tested based on its capacitive effect while charging up and the amount of current at each electrode finger at the saturation stage. The hardware enablers for this test were the multimeter and LCR meter. Arduino microprocessor was used to test and measure the transient response time for each electrode finger. The transient response sensitivity of the electrode sensor is measured to be 0.0873 millisecond/cm while the resolution of the electrode sensor is 0.1cm over a range of 30cm water level. A multiple correlation of 0.921 was achieved for the water level, measured current and measured capacitance with P-values less than 0.05 indicating strength of the data obtained from the tests conducted. The result showed strong evidence that the electrode water level sensor can be an alternative method of measuring water level.
Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abd Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.957 KB) | DOI: 10.11591/eei.v8i2.1504

Abstract

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.
Capacitive electrode sensor implanted on a printed circuit board designed for continuous water level measurement Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abdul Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.756 KB) | DOI: 10.11591/eei.v8i2.1515

Abstract

Water level sensors are one of the practical ways to get the actual measurement of the depth of a dam or canal. The ease of deployment and easy data acquisition makes them widely used in many fields. Therefore, it will be advantageous to have a miniaturized water level sensor for easier mobility and deployment. A novel method for measuring water level using a Printed Circuit Board has been proposed in this paper. The design stages of circuit sketching, printing of sketch on PCB and etching are discussed for the electrode water level sensor. A signal conditioning circuit is necessary to maintain a steady flow of current from the power source. The fabricated electrode water level sensor was tested based on its capacitive effect while charging up and the amount of current at each electrode finger at the saturation stage. The hardware enablers for this test were the multimeter and LCR meter. Arduino microprocessor was used to test and measure the transient response time for each electrode finger. The transient response sensitivity of the electrode sensor is measured to be 0.0873 millisecond/cm while the resolution of the electrode sensor is 0.1cm over a range of 30cm water level. A multiple correlation of 0.921 was achieved for the water level, measured current and measured capacitance with P-values less than 0.05 indicating strength of the data obtained from the tests conducted. The result showed strong evidence that the electrode water level sensor can be an alternative method of measuring water level.
Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abd Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.957 KB) | DOI: 10.11591/eei.v8i2.1504

Abstract

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.
Co-Authors Mohamed Hadi Habaebi Noreha Abd Malik Noreha Abdul Malik