Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

  • Talha Ahmed Khan Universiti Kuala Lumpur Usman Institute Of Technology
  • Kushsairy Kadir Universiti Kuala Lumpur
  • Muhammad Alam Universiti Kuala Lumpur Ilma University
  • Zeeshan Shahid Institute Of Business Management
  • M.S Mazliham Universiti Kuala Lumpur
Keywords: Flash flood prediction, soil flux, natural disaster, Disaster management, neural network, Carbon dioxide level

Abstract

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods.

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Author Biographies

Talha Ahmed Khan, Universiti Kuala Lumpur Usman Institute Of Technology

Universiti Kuala Lumpur,

 Jalan Sungai Pusu, Gombak, Kuala Lumpur, 53100, MALAYSIA

 

Usman Institute Of Technology,

 ST-13, Block 7 Gulshan-e-Iqbal, Karachi, 75300, PAKISTAN

Kushsairy Kadir, Universiti Kuala Lumpur

Universiti Kuala Lumpur,

 Jalan Sungai Pusu, Gombak, Kuala Lumpur, 53100, MALAYSIA

Muhammad Alam, Universiti Kuala Lumpur Ilma University

Universiti Kuala Lumpur,

 Jalan Sungai Pusu, Gombak, Kuala Lumpur, 53100, MALAYSIA

 

Ilma University,

 Korangi Road, Korangi creek, Karachi, 75190, PAKISTAN

Zeeshan Shahid, Institute Of Business Management

Institute Of Business Management,

 Korangi creek, Karachi, 75190, PAKISTAN

M.S Mazliham, Universiti Kuala Lumpur

Universiti Kuala Lumpur,

 Jalan Sungai Pusu, Gombak, Kuala Lumpur, 53100, MALAYSIA

Published
26-11-2018
How to Cite
Ahmed Khan, T., Kadir, K., Alam, M., Shahid, Z., & Mazliham, M. (2018). Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate. International Journal of Integrated Engineering, 10(7). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/3484