A Novel Approach for the Investigation of Flash Floods using Soil Flux and CO2: An Implementation of MLP with Less False Alarm Rate

Extreme intensified flash floods and runoffs are the main root cause for the high number of casualties, properties and values belongings deficit within few seconds in so many countries. Flash floods are acknowledged as the most dangerous and unpredictable disasters in the whole world. Floods can be due to the heavy precipitation velocity, reservoir break, high oceanic waves at sea shore or snow melts briskly in oceans. Many techniques have been followed to investigate and forecast the flash floods on urgent basis. Almost dozens of sensors have been used to detect all the evaluation parameters concerned to 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 PIR sensors have also been used for this purpose. Transducers and sensing elements generate false alarms or missed information as well due to the inadequate 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 phenomenon the soil is saturated briskly causes more floods and run-offs. In our research we have discarded the fake alerts and reduced the false alarms by using classifier multilayer perceptron (MLP). Experimental results demonstrated that MLP performed better than the other existing techniques.