A review on application of data mining techniques to combat natural disasters

Abstract

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:-

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

“…The possibility of predicting which areas are susceptible to a specific type of disaster, including landslides or forest fires, is undisputed. The prediction techniques have proven valuable for predicting various characteristics of a natural disaster that has occurred 10 . Many researchers recognized that the occurrence of landslides and forest fires is influenced by various aspects that involve human activities and climate conditions 11,12 .…”

“…Flowchart of methodology used for multi-hazard spatial modeling in the Fars Province, Iran. Scientific RepoRtS | (2020) 10:3203 | https://doi.org/10.1038/s41598-020-60191-3www.nature.com/scientificreports www.nature.com/scientificreports/ TP/(TN TP FP FN)) 100(10) …”

Data Mining with R: An Applied Study

“…They stated that logistic regression yielded the most successful result according to sensitivity criterion. Goswami et al (2018), in their compilation studies on the application of data mining techniques, found that there are not enough resources for natural disaster detection especially in the Indian region. This study reveals the necessity of data mining in combating natural disasters.…”

An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping

“…As mentioned before, natural hazard assessment has been investigated by conventional ML approaches and promising results have been achieved [29]. Yet the success of these models has not attained enough maturity.…”