A deep neural network modeling framework to make early predictions about the onset of floods due to increase in water levels in rivers, oceans, and other water bodies. The model takes into account all the available predictive potentials within a region to make the best prediction of increase in water level in water bodies. Such predictive potentials for a coastal area are the harmonic tide and the past water levels recorded by one or multiple meteorological stations. For some areas where operational forecasting of physical ocean models exist, the results will also be taken as predictive features for training the model. Non-water level types of data - rainfall,wind, air pressure, temperature, and salinity are also explored due to their effects on the onset of floods.
Our model will monitor the changes in the parameters that may lead to flooding and predict the onset of floods in advance. The model maintains a score of occurence of flood in different areas in the near future. Whenever the score exceeds a specified threshold value for a particular area, the system will highlight that particular area and warn the people as well as authorities of that particular area in advance.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent