Prediction of Natural Disasters: Many natural objects in the Earth, like rock fractures or drainage networks, have fractal geometry. We can measure the past events and can find the probability forecasts about the size, location and timing of future natural disasters. By comparing the fractal formulas of the size and frequency of a hurricane’s wind speed to the historic record of information about past hurricane landfall location and timing we can predict the approximate wind speed of the hurricane when it made landfall at a given coastal location. Forecasts of hazardous natural phenomena based on the application of fractals can be made available to government agencies responsible for planning and responding to natural disasters and other emergency personnel to better forecast the size, location, and timing of future events. Gravitational mass flows, such as avalanches, debris flows are common events in alpine regions with high impact on transport routes. We will use Hazard Zone Maps to systematically approach this threat. These maps mark vulnerable zones in habitable areas to allow effective planning of hazard mitigation measures and development of settlements. Hazard zone maps have shown to be an effective tool to reduce fatalities during extreme events. They are created in a complex process, based on experience, empirical models, physical simulations and historical data. We interpret the task of hazard zone mapping as a classification problem. Every point in a specific area has to be classified according to its vulnerability. We use a Convolutional Neuronal Network (CNN) to identify terrain formations with the potential for catastrophic snow avalanches and label points in their reach as vulnerable. Repeating this procedure for all points allows us to generate an artificial hazard zone map. The places which will be marked as prone to natural disasters can be monitored regularly.

Detection of Natural Disasters – To enhance the rescue activities and saving lives; disaster monitoring and management plays a critical role. Remote sensing to monitor natural disasters has been proven useful for detection of earthquakes, land sliding, flooding, wildfire and volcanic activity. We will use CNN to identify natural disasters when fed satellite images. An accurate system which is capable of tagging the type of disaster using close to real time satellite images will lead to better response and relief to the impacted areas.

To Manage the impact of natural disasters— We analyze Twitter data to identify smaller disaster-related events, known as sub-events, and generate highly accurate, real-time summaries that can be used to guide response activities. Analyzing this data and using it to generate reports related to a sub-topic of a disaster—such as infrastructure damage or shelter needs—could help humanitarian organizations better respond to the varying needs of individuals in an affected area.Several works on disaster-specific summarization in recent times proposed algorithms that mostly provide a general summary of the whole event. "However, different stakeholders like rescue workers, government agencies, field experts, common people have different information needs."