Flooding Level Classification by Gait Analysis of Smartphone Sensor Data

Urban flooding is a common problem across the world. In India, it leads to casualties every year, and financial loss to the tune of tens of billions of rupees. The damage done due to flooding can be mitigated if the locations deserving attention are known. This will enable an effective emergency response, and provide enough information for the construction of appropriate storm water drains to mitigate the effect of floods. In this work, a new technique to detect flooding level is introduced, which requires no additional equipment, and consequent installation and maintenance costs. The gait characteristics in different flooding levels have been captured by smartphone sensors, which are then used to classify flooding levels. In order to accomplish this, smartphone sensor readings have been taken by 12 volunteers in pools of different depths, and have been used to train machine learning models in a supervised manner. Support vector machines, random forests and naïve bayes models have been attempted, of which, support vector machines perform best with a classification accuracy of 99.45%. Further analysis of the most relevant features for classification agrees with our intuition of gait characteristics in different depths.