Miniaturized Water Flow and Level Monitoring System for Flood Disaster Early Warning

Abdullahi, Salami Ifedapo; Habaebi, Mohamed Hadi; Gunawan, Teddy Surya; Islam, Rafiqul

doi:10.1088/1757-899x/260/1/012019

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…One of the few ways to attain data are through open data and sensor data readings which have been carried out by most researchers in [5,6,11,14,21]. The deployment of an embedded system which adopts IoT and Neural network has been critically analysed in [6].…”

Section: Introductionmentioning

confidence: 99%

“…A flow rate sensor and a water level sensor were utilized in the process of data acquisition while prediction model was done to make a system that can predict flood occurrence. Researchers have used statistical means to analyse flood data in [5,7] while the use of ANN and other hybrid system integrating fuzzy system and neural network have been heavily explored in [8][9][10][11][12][13][14]. The use of Boosted regression tree for analysis was carried out in [8] to analyze reservoir inflow.…”

Section: Introductionmentioning

confidence: 99%

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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

2019

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

2019

Self Cite

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“…Researchers have heavily investigated on different mechanical flow sensors in [3][4][5][6] which are used to detect velocity of water current. [6] used a mechanical flow sensor to get the inflow rate of water entering the inlet canal while investigating the performance of the flow sensor in detecting the correct flow rate.…”

Section: Introductionmentioning

confidence: 99%

Design, simulation and practical experimentation of miniaturized turbine flow sensor for flow meter assessment

Abdullahi¹,

Habaebi²,

Malik³

2019

Bulletin EEI

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Flow sensors are very essential in many aspects of our daily lives. Many of the industrial processes need a very consistent flow sensor to monitor and check for irregularities in their system. Therefore, flow sensor is an important tool for advanced operation in industrial environment. In this paper, the design and development of a 3D fabricated flow sensor was carried out using SolidWork 3D CAD. SolidWork Flow Simulation was used to model the effect the turbine flow sensor would have on a constant flowing water while MATLAB Simulink flow graph was created to visualize the effect of turbine flow sensor response with voltage input. Afterwards, the design was 3D printed using UP Plus 2 3D printer. The experimentation involved selection of sensors, coding to control the turbine flow sensor and automatic data logging and storage. During the design phase, the sensors and actuators were assembled using locally sourced material. Subsequently, under controlled laboratory environment, the turbine flow sensor was tested using a DC motor which was programmed to control the revolution per minute(rpm) of the turbine flow sensor. The rpm and velocity of the turbine flow meter was measured and stored in a database via Microsoft Excel using Cool Term Software. A total number of 517 readings were analysed to evaluate the performance of the turbine flow sensor. The result shows that the turbine flow meter is responsive to the motor input voltage and yielded accurate measurement of rpm and velocity of turbine flow meter.

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“…Contact, point level sensors include Float Switch, Capacitance sensor, Conductivity probes and Magnetic Level Gauges [2]. Water level measurements are now widely used for various things such as measuring the depth of rivers, tanks and irrigation of agricultural lands [3,4]. In recent years, the use of optical fibers to measure water level has gradually increased.…”

Section: Introductionmentioning

confidence: 99%

Capacitive electrode sensor implanted on a printed circuit board designed for continuous water level measurement

2019

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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.

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Miniaturized Water Flow and Level Monitoring System for Flood Disaster Early Warning

Cited by 9 publications

References 12 publications

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

Design, simulation and practical experimentation of miniaturized turbine flow sensor for flow meter assessment

Capacitive electrode sensor implanted on a printed circuit board designed for continuous water level measurement

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