Flood Detection Using Gradient Boost Machine Learning Approach

Floods are a natural calamity which leads the dry land to be submerged by water due to a resurgence of a waterbody capacity which goes well beyond its natural limits leading to an overflow. Floods are most commonly caused by excessive precipitation and runoffs which lead the adjoining land areas to be submerged by water which causes huge loss to human lives and infrastructure, which includes damaging buildings, bridges, power supply network and crippling the transportation and bringing economic hardships on the people. Over the years, multiple measures have been taken to predetermine flood warnings which have been implemented using sensor technology and active monitoring of the parameters. This had led to the creation of a wide number of data-sets which can be employed for future purposes and with the availability of data analytics techniques heralded by the resurgence of Machine Learning and the concept of Intelligent Machines, the datasets can be directly employed to allow algorithms to "learn" directly from the collected data and based upon this, create a predetermined equation as a model to help predict future outcomes. In the proposed method, we propose a Flood Detection mechanism using the Gradient Boost Algorithm which will be used to classify the data sets and perform regression on it to produce the best outcomes from the datasets we will use to train it, to create a weak prediction model based on a Decision Tree. The outcome can henceforth be used to display it to the concerned authorities who can employ preemptive actions to tackle the threat. This approach is developed to be better suited in such ends providing predictions with high accuracy and additionally employs various other technologies like Remote Sensing and Sensor Technology to develop accurate datasets required to train the model.