Role of Predictive Data Analytics to Assess Long Term Impacts of Disaster

Abstract

Disaster management is one of the promising fields in research where future predictions and their effects are suggested based on the processing and analysis of historical data sets. Analyze and detect the long-term effects of a disaster is a very crucial and challenging task. At present, the governments, as well as private agencies, are facing a lot of difficulties while analyzing the disaster data. Because of that assessment of long-term effects for various parameters affecting education, job, employability, agriculture, social, etc., and also the impact on peoples living in those areas are difficult to analyze. This paper uncovers the framework to assess the long-term effects of disaster using trending Machine Learning algorithms i.e., XG Boost algorithms, Modified Random Forest, Modified SVM. The proposed framework analyzes the historical disaster data sets, its effects and predicts the impacts on various factors in case of occurrence of a similar disaster in the present scenario. This framework will also recommend the tentative safety measures and future impacts to the concerned authorities in case of a plausible disaster. At last, outcomes of the proposed framework will provide long-term impact analysis and recommendations through comparative analysis with different parameters which are derived from historical data sets.