The objective of our project is to predict and prevent the flood dynamically using IOT.
The Scope of our project Effective Prediction and Prevention Analysis is like we are not only going to predict the dam level and also we are going to prevent the city from flood. Saving the city means not only saving people but also all the resources in our city. In prediction process we take the present dam situation as input for this and then we perform our calculation and finally we predict the future situation of the dam. This prediction process helps us to decide whether we need to undergo prevention process to save our city. The prevention process how we are going to do is, we will communicate with all the dams in the city and will perform an algorithm for this process. So that we can predict the situation which leads to save our people getting affected from the flood.
Climate change due to global warming has caused an increase in unpredictability of weather patterns in the world today. It has brought tremendous impact on the dam due to severity of abrupt rainfall and gigantic water flow from canals as well as rain resulting in large discharge of water may cause flood. Floods are responsible for the loss of precious lives and destruction of large amounts of property every year, especially in the poor and developing countries, where people are at the mercy of natural elements. A lot of effort has been put in developing systems which help to minimize the damage through early disaster predictions. As a network for the prediction model has to be deployed in the rural areas, there is a severe limitation of resources like money, power and skilled manpower. The goal of EPPF project is to predict and prevent the flood dynamically using IOT system. The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. Here all the data is monitored and analysed in cloud. Two things we considered here as the input which is water flow from the canal and rain water level from rainfall, by using these two inputs calculation and analysis as well as prediction, prevention has been done on this project. Water level Prediction - We have considered our prediction algorithm to be compatible with a centralized framework based network architecture which consists of a number of sensor nodes collecting different data required for prediction through water flow rainfall sensors and others. These data is communicated to the single computation node (or an administrative office as the case may be) which acts as the cluster head bearing the responsibility of most of the computations and predictions required. With data collected to cloud dynamically, forecasts are made using our DARS algorithm. Prevention - Here decision making is done according to the availability of water in neighbouring dam.