Over 2.3 Billion people are affected due to floods in last 20 years and causing countless death , More than 92,million cattle are lost every year, seven million hectares of land is affected, and damage is over trillions dollars when taken globally in last 5 years. Floods are complicated natural events. It depends on several parameters, so it is very difficult to model analytically. The floods in a catchment depends on the characteristics of the catchment, rainfall and antecedent conditions. So the estimation of the flood peak is a very complex problem. Its due to the lack of Flood Prediction System which can predict the situation accurately. To Overcome this challenge we are building a Flood Prediction System using Predictive modelling. However we have divided our idea into small fragments but enough to be used globally. We have considered most flooded state of India, but can be used widely for all the low lying geographical regions.
•The plains of Bihar, adjoining Nepal, are drained by a number of rivers that have their catchments in the steep and geologically nascent Himalayas. Kosi, Gandak,Burhi Gandak, Bagmati, Kamla Balan, Mahananda and Adhwara Group of rivers originates in Nepal, carry high discharge and very high sediment load and drops it down in the plains of Bihar.
About 65% of catchments area of these rivers falls in Nepal/Tibet and only 35% of catchments area lies in Bihar.
Bihar is India’s most flood-prone State, with 76 percent of the population, in the north Bihar living under the recurring threat of flood devastation. About 68800 sq Km out of total geographical area of 94163 sq Km comprising 73.06 percent is flood affected.
According to some historical data, 16.5% of the total flood affected area in India is located in Bihar while 22.1% of the flood affected population in India lives in Bihar.
From 1979 to Present day more than 8,873 Humans & 27,573 animals have lost their life due to flood.
Some of Tools & Technology which is being used & can be used for Flood Prediction:
•IBM. Watson Studio democratizes machine learning and deep learning to accelerate infusion of AI in to drive innovation.
•An Intelligent Hydro-informatics Integration Platform for Regional Flood Inundation Warning Systems.
•Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm.
Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation