While natural disasters are plaguing our planet from centuries, in India, floods are the major disasters which causes massive loss to life and property. Due to the heavy southwest monsoon rains and distended river banks, almost all of India is flood-prone. Extreme precipitation events such as flash floods and torrential rains have become common in central and south India from past few decades. To mitigate the effects of floods, both structural and non-structural measures can be employed, such as dams, dykes, channelisation, flood proofing of properties, land-use regulation and flood warning schemes. Earlier advanced warning can be achieved through mathematical modelling of various data which results in the occurrence of floods.
We are intending to tackle the problem of floods with a three way approach.
Prediction - Flash floods are generally caused due to heavy rainfall and poor management. Hence, we would build a model to predict the probability of flood occurrence based on weather data, rainfall forecast and dam water levels in the country. We are looking at Artificial Intelligence techniques such as the Artificial Neural Networks & Support Vector Machines to build the prediction model. The ANN has been found suitable for modelling the rainfall-runoff process in a wide variety of catchments under specific circumstances. Rainfall-Runoff modelling is a mathematical model describing relations of a rainfall catchment area, drainage basin or watershed with rainfall parameters. It basically produces a surface runoff hydrograph in response to a rainfall event which will be used in flood forecast. We intend to research about Recurrent Neural Networks, of Elman and Jordan forms specifically and compare it with the basic Feed Forward Networks and Multilayer Perceptrons since it has been found to give better results according to latest research from Institute for Water Education. If everything goes well, we will be implementing using the RNN algorithm.
Management - A chrome extension to block fake news regarding death tolls and places affected. We want to create a one stop solution to get a centralized common accurate news.