Flood forecasting using Internet of things and Artificial Neural Networks

Floods are the most common natural disasters, and cause significant damage to life, agriculture and economy. Research has moved on from mathematical modeling or physical parameter based flood forecasting schemes, to methodologies focused around algorithmic approaches. The Internet of Things (IoT) is a field of applied electronics and computer science where a system of devices collects data in real time and transfers it through a Wireless Sensor Network (WSN) to the computing device for analysis. IoT generally combines embedded system hardware techniques along with data science or machine learning models. In this work, an IoT and machine learning based embedded system is proposed to predict the probability of floods in a river basin. The model uses a modified mesh network connection over ZigBee for the WSN to collect data, and a GPRS module to send the data over the internet. The data sets are evaluated using an artificial neural network model. The results of the analysis which are also appended show a considerable improvement over the currently existing methods.