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.
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%.
This paper proposed an alternative method of measuring water level using a Printed Circuit Board(PCB). The design of the electrode water level sensor went through circuit sketching, printing of sketch on PCB and etching. The signal conditioning circuit board was fabricated using a donut board and other electrical components. Experimentation was carried on the fabricated electrode sensor and the capacitance and current for each electrode finger was measured using digital multimeter and LCR meter. The multiple correlation of the water level, measured current and measured capacitance produced a value of 0.921 withP-values less than 0.05 showing the strength of the data obtained from the test conducted. The electrode water level sensor has proven to be consistent and reliable under normal working condition.
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