Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life etc.due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for i) prediction ii) detection and iii) development of appropriate disaster management strategy based on the collected data from disasters. A detailed description of availability of data from geological observatories (seismological, hydrological), satellites, remote sensing and newer sources like social networking sites as twitter is presented. An extensive and in depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters. Finally a framework for building a disaster management database for India hosted on open source Big Data platform like Hadoop in a phased manner has been proposed.not only the immediate effect as observed in [61], exposure to a natural disaster in the past months increases the likelihood of acute illnesses such as diarrhea, fever, and acute respiratory illness in children under 5 year by 9-18%.. The socioeconomic status of the households has a direct bearing on the magnitude and nature of these effects. The disasters have pronounced effects on business houses as well. As stated in [50] 40% of the companies, which were closed for consecutive 3 days, failed or closed down within a period of 36 months. The disasters are not infrequent as well.Only for earthquake [7], there are as many as 20 earthquakes every year which has a Richter scale reading greater than 7.0. The effects of the disasters are much more pronounced in developing countries like India. Meteorologist,Geologists, Environmental Scientists, Computer Scientistsand scientists from various other disciplines have put a lot of concerted efforts to predict the time, place and severity of the disasters. Apart from advanced weather forecasting models, data mining models also have been used for the same purpose. Another line of research, has concentrated on disaster management, appropriate flow of information, channelizing the relief work and analysis of needs or concerns of the victims. The sources of the underlying data for such tasks have often been social media and other internet media.Diverse data are also collected on regular basis by satellites, wireless and remote sensors, national meteorological and geological departments, NGOs, various other international, government and private bodies, before, during and after the disaster. The data thus collected qualifies to be called "Big Data" because of the volume, variety and the velocity in which the data are generated. A brief technical description of some of the major natural disasters:-

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Abstract. To reduce the uncertainty in climatic impacts induced by black carbon (BC) from global and regional aerosol–climate model simulations, it is a foremost requirement to improve the prediction of modelled BC distribution, specifically over the regions where the atmosphere is loaded with a large amount of BC, e.g. the Indo-Gangetic Plain (IGP) in the Indian subcontinent. Here we examine the wintertime direct radiative perturbation due to BC with an efficiently modelled BC distribution over the IGP in a high-resolution (0.1∘ × 0.1∘) chemical transport model, CHIMERE, implementing new BC emission inventories. The model efficiency in simulating the observed BC distribution was assessed by executing five simulations: Constrained and bottomup (bottomup includes Smog, Cmip, Edgar, and Pku). These simulations respectively implement the recently estimated India-based observationally constrained BC emissions (Constrainedemiss) and the latest bottom-up BC emissions (India-based: Smog-India; global: Coupled Model Intercomparison Project phase 6 – CMIP6, Emission Database for Global Atmospheric Research-V4 – EDGAR-V4, and Peking University BC Inventory – PKU). The mean BC emission flux from the five BC emission inventory databases was found to be considerably high (450–1000 kg km−2 yr−1) over most of the IGP, with this being the highest (> 2500 kg km−2 yr−1) over megacities (Kolkata and Delhi). A low estimated value of the normalised mean bias (NMB) and root mean square error (RMSE) from the Constrained estimated BC concentration (NMB: < 17 %) and aerosol optical depth due to BC (BC-AOD) (NMB: 11 %) indicated that simulations with Constrainedemiss BC emissions in CHIMERE could simulate the distribution of BC pollution over the IGP more efficiently than with bottom-up emissions. The high BC pollution covering the IGP region comprised a wintertime all-day (daytime) mean BC concentration and BC-AOD respectively in the range 14–25 µg m−3 (6–8 µg m−3) and 0.04–0.08 µg m−3 from the Constrained simulation. The simulated BC concentration and BC-AOD were inferred to be primarily sensitive to the change in BC emission strength over most of the IGP (including the megacity of Kolkata), but also to the transport of BC aerosols over megacity Delhi. Five main hotspot locations were identified in and around Delhi (northern IGP), Prayagraj–Allahabad–Varanasi (central IGP), Patna–Palamu (upper, lower, and mideastern IGP), and Kolkata (eastern IGP). The wintertime direct radiative perturbation due to BC aerosols from the Constrained simulation estimated the atmospheric radiative warming (+30 to +50 W m−2) to be about 50 %–70 % larger than the surface cooling. A widespread enhancement in atmospheric radiative warming due to BC by 2–3 times and a reduction in surface cooling by 10 %–20 %, with net warming at the top of the atmosphere (TOA) of 10–15 W m−2, were noticed compared to the atmosphere without BC, for which a net cooling at the TOA was exhibited. These perturbations were the strongest around megacities (Kolkata and Delhi), extended to the eastern coast, and were inferred to be 30 %–50% lower from the bottomup than the Constrained simulation.

A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC health impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including successfully predicted BC concentrations. Population exposure to BC is notable, with more than 60 million people identified as living in hotspots of BC concentration (wintertime mean, >20 μg m −3 ). The attributable fraction of the total cardiovascular disease mortality (CVM) burden to BC exposures is 62% for the megacity. The semiurban area comprised about 49% of the total BC-attributable CVM burden over the IGP. More than 400,000 lives can potentially be saved from CVM annually by implementing prioritized emission reduction from the combustion of domestic biofuel in the semiurban area, diesel oil in transportation, and coal in thermal power plant and brick kiln industries in megacities.